Annex II: CAPSIL Wiki Copy

Transcription

Annex II: CAPSIL Wiki Copy
June 1 2009
Table of Contents
1 CAPSIL Overview...............................................................................................................................................................................................................1
1.1 CAPSIL Meetings............................................................................................................................................................................................1
1.2 CAPSIL Members............................................................................................................................................................................................1
1.3 CAPSIL Links..................................................................................................................................................................................................1
1.4 CAPSIL Executive Summary...........................................................................................................................................................................1
2 CAPSIL Meeting London, UK............................................................................................................................................................................................3
2.1 Objectives (as per project Anex 1)..................................................................................................................................................................3
2.2 Working Meeting Part 1...................................................................................................................................................................................3
2.3 CAPSIL General Assembly.............................................................................................................................................................................3
2.4 Working Meeting Part 2...................................................................................................................................................................................3
2.5 Directions to the campus/ meeting venue.......................................................................................................................................................3
3 CAPSIL Meeting Tokyo, Japan.........................................................................................................................................................................................4
3.1 CAPSIL Contract Objectives...........................................................................................................................................................................4
4 CAPSIL Meeting EU...........................................................................................................................................................................................................5
5 CAPSIL Meeting US...........................................................................................................................................................................................................6
5.1 CAPSIL Contract Objectives...........................................................................................................................................................................6
6 CAPSIL Meeting other.......................................................................................................................................................................................................7
6.1 Objectives........................................................................................................................................................................................................7
7 Introduction to CAPSILs of knowledge...........................................................................................................................................................................8
8 COPD..................................................................................................................................................................................................................................9
9 Congestive Heart Failure................................................................................................................................................................................................11
9.1 Issues............................................................................................................................................................................................................11
9.2 Justification....................................................................................................................................................................................................11
9.3 Scientific (Basis/efficacy/evidence)...............................................................................................................................................................11
9.4 Research.......................................................................................................................................................................................................11
9.5 Commercial...................................................................................................................................................................................................12
9.6 Standards......................................................................................................................................................................................................13
9.7 Gaps..............................................................................................................................................................................................................14
9.8 Future Vision.................................................................................................................................................................................................14
9.9 Issues............................................................................................................................................................................................................14
9.10 References..................................................................................................................................................................................................15
10 Obesity............................................................................................................................................................................................................................17
11 Dementia.........................................................................................................................................................................................................................18
12 Depression.....................................................................................................................................................................................................................19
13 Osteoarthritis.................................................................................................................................................................................................................20
13.1 Research.....................................................................................................................................................................................................20
14 Parkinson's Disease......................................................................................................................................................................................................21
15 Stroke..............................................................................................................................................................................................................................23
16 Falls Prevention.............................................................................................................................................................................................................24
16.1 Falls Detection.............................................................................................................................................................................................24
16.2 Falls Prevention...........................................................................................................................................................................................24
16.3 Issues..........................................................................................................................................................................................................25
16.4 Justification..................................................................................................................................................................................................25
16.5 Research.....................................................................................................................................................................................................25
16.6 Projects........................................................................................................................................................................................................25
16.7 References..................................................................................................................................................................................................25
17 Stroke Rehab Management...........................................................................................................................................................................................26
18 Weight Management......................................................................................................................................................................................................28
19 Cognitive Training.........................................................................................................................................................................................................31
19.1 General Description.....................................................................................................................................................................................31
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Table of Contents
20 Social Connectedness..................................................................................................................................................................................................36
20.1 Online Social Networks...............................................................................................................................................................................37
20.2 Use Case.....................................................................................................................................................................................................39
20.3 Online Social Networking Products.............................................................................................................................................................41
20.4 Viability of Social Networks for Elders.........................................................................................................................................................46
20.5 References..................................................................................................................................................................................................48
21 Activity Monitoring........................................................................................................................................................................................................50
21.1 General Description.....................................................................................................................................................................................50
21.2 Justification..................................................................................................................................................................................................51
21.3 Scientific Basis............................................................................................................................................................................................51
21.4 Research.....................................................................................................................................................................................................52
21.5 References..................................................................................................................................................................................................53
22 Driving Assistance........................................................................................................................................................................................................54
22.1 Issues..........................................................................................................................................................................................................54
22.2 Justification..................................................................................................................................................................................................54
22.3 Research.....................................................................................................................................................................................................54
22.4 Commercial.................................................................................................................................................................................................54
22.5 Gaps............................................................................................................................................................................................................55
22.6 Future Vision...............................................................................................................................................................................................55
22.7 References..................................................................................................................................................................................................55
23 Robotics.........................................................................................................................................................................................................................56
23.1 Robots for the Aged Society........................................................................................................................................................................56
23.2 Issues..........................................................................................................................................................................................................56
23.3 Justification..................................................................................................................................................................................................56
23.4 Scientific Basis/efficacy/evidence)...............................................................................................................................................................57
23.5 Research projects........................................................................................................................................................................................57
23.6 Commercial.................................................................................................................................................................................................57
23.7 Standards....................................................................................................................................................................................................58
23.8 Gaps............................................................................................................................................................................................................58
23.9 Future Vision...............................................................................................................................................................................................59
23.10 References................................................................................................................................................................................................59
24 Continence.....................................................................................................................................................................................................................60
25 Smoking Cessation.......................................................................................................................................................................................................61
25.1 References..................................................................................................................................................................................................61
26 Sensors...........................................................................................................................................................................................................................62
26.1 Overview......................................................................................................................................................................................................62
26.2 Key Features...............................................................................................................................................................................................62
26.3 Sensor Nodes..............................................................................................................................................................................................62
26.4 Design Considerations................................................................................................................................................................................62
26.5 Resources...................................................................................................................................................................................................63
26.6 References..................................................................................................................................................................................................63
27 Data Processing.............................................................................................................................................................................................................64
27.1 On-node Data Processing...........................................................................................................................................................................64
27.2 Sensor Fusion.............................................................................................................................................................................................64
27.3 Context Aware and Autonomic Sensing......................................................................................................................................................64
27.4 Data Mining and Trend Analysis..................................................................................................................................................................64
27.5 Falls Detection Algorithms...........................................................................................................................................................................64
28 Standards.......................................................................................................................................................................................................................65
28.1 Advantages..................................................................................................................................................................................................65
28.2 Disadvantages.............................................................................................................................................................................................65
29 Initiatives.......................................................................................................................................................................................................................66
29.1 Bluetooth SIG..............................................................................................................................................................................................66
29.2 USB Personal Health Device Specification.................................................................................................................................................66
29.3 ISO/IEEE Standards for Personal Health....................................................................................................................................................66
29.4 CEN.............................................................................................................................................................................................................66
29.5 Routing over Low-power and Lossy Networks (Roll)...................................................................................................................................66
29.6 Integrating the Healthcare Enterprise (IHE)................................................................................................................................................67
29.7 Health Level 7 (HL7)....................................................................................................................................................................................67
29.8 Continua - Promoting Personal Health Systems Interoperability.................................................................................................................67
29.9 References..................................................................................................................................................................................................67
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30 Connectivity...................................................................................................................................................................................................................68
30.1 Technologies for Connectivity.....................................................................................................................................................................68
30.2 Inter-Network Connectivity..........................................................................................................................................................................68
30.3 Backhaul Network Access Technologies.....................................................................................................................................................69
30.4 Government Policy......................................................................................................................................................................................71
30.5 References..................................................................................................................................................................................................72
31 Digital Health Records..................................................................................................................................................................................................73
31.1 Introduction..................................................................................................................................................................................................73
31.2 Electronic Medical Records (EMRs) and Electronic Health Records (EHRs)..............................................................................................73
31.3 Components of an Electronic Medical Record............................................................................................................................................73
31.4 Issues..........................................................................................................................................................................................................74
31.5 Benefits........................................................................................................................................................................................................75
31.6 Research.....................................................................................................................................................................................................75
31.7 US................................................................................................................................................................................................................75
31.8 EU................................................................................................................................................................................................................75
31.9 Japan...........................................................................................................................................................................................................75
31.10 Projects......................................................................................................................................................................................................75
31.11 Commercial Products................................................................................................................................................................................75
31.12 Personal Health Records...........................................................................................................................................................................75
31.13 Electronic Medical Records.......................................................................................................................................................................75
31.14 Patient/User ID..........................................................................................................................................................................................76
31.15 Players (links to VCs/Angels/Agencies/MNCs/SME).................................................................................................................................76
31.16 Business Models.......................................................................................................................................................................................76
31.17 Standards..................................................................................................................................................................................................76
31.18 HL7............................................................................................................................................................................................................76
31.19 Gaps..........................................................................................................................................................................................................76
31.20 Gaps in technology....................................................................................................................................................................................76
31.21 Gaps in the basic science..........................................................................................................................................................................76
31.22 Gaps in operation......................................................................................................................................................................................76
31.23 Gaps in implementation.............................................................................................................................................................................76
31.24 Future Vision.............................................................................................................................................................................................76
31.25 References................................................................................................................................................................................................76
32 User Centered Design for Independent Living............................................................................................................................................................77
32.1 Introduction..................................................................................................................................................................................................77
32.2 Issue............................................................................................................................................................................................................77
32.3 Functional limitations...................................................................................................................................................................................77
32.4 Research.....................................................................................................................................................................................................78
32.5 Commercial.................................................................................................................................................................................................78
32.6 References..................................................................................................................................................................................................78
33 Privacy & Security.........................................................................................................................................................................................................79
33.1 Fundamentals of Freedom..........................................................................................................................................................................79
33.2 OECD Guidelines on Privacy......................................................................................................................................................................79
33.3 Privacy Concerns of Wireless Sensor Networks.........................................................................................................................................79
33.4 Not Everyone is Enthusiastic About this Technology..................................................................................................................................80
33.5 Global Policy and Legislative Efforts...........................................................................................................................................................80
33.6 European Union - Directive 95/46/EC (5)....................................................................................................................................................80
33.7 Implementation by the member states........................................................................................................................................................82
33.8 United States...............................................................................................................................................................................................82
33.9 Interoperability Between Health Information Systems - Health Level 7 (HL7).............................................................................................83
33.10 Applicable Policy Concerning Wireless Technologies...............................................................................................................................83
33.11 References................................................................................................................................................................................................83
34 Ethics..............................................................................................................................................................................................................................85
35 Sports.............................................................................................................................................................................................................................87
35.1 Sports and Wellbeing..................................................................................................................................................................................87
35.2 Sport Application Examples.........................................................................................................................................................................87
35.3 Monitoring....................................................................................................................................................................................................87
35.4 Challenges and Issues................................................................................................................................................................................88
36 Business Models...........................................................................................................................................................................................................90
36.1 Review of Reimbursement Models for Telehealthcare................................................................................................................................90
36.2 USA Telehealthcare Reimbursement Models..............................................................................................................................................90
36.3 European Telehealth Reimbursement Models............................................................................................................................................90
36.4 Examples of European Reimbursement Policies by Country......................................................................................................................90
36.5 Telehealthcare Business Models - The Opportunity and Markets...............................................................................................................91
36.6 Chronic Disease Programs - The Way Forward..........................................................................................................................................91
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36 Business Models
36.7 Telehealth Business Models Are Not Just About Vitals Measurements......................................................................................................91
36.8 Examples of Telehealth Solutions That Preserve the Human to Human Relationship................................................................................91
37 Proactive Models of Telehealthcare............................................................................................................................................................................92
37.1 Example of 'Proactive' Healthcare System..................................................................................................................................................92
37.2 Business Model Needs to Include Some Form of Assessment for Suitability.............................................................................................92
37.3 References..................................................................................................................................................................................................92
38 Government Policy........................................................................................................................................................................................................93
38.1 Barriers to Telehealth Adoption Where Government Policy Can Help........................................................................................................93
38.2 Cost and Reimbursement............................................................................................................................................................................93
38.3 Demonstrating the Benefits - Large Scale Pilots.........................................................................................................................................93
38.4 Data Ownership and Legal Concerns..........................................................................................................................................................94
38.5 Perception and Attitudes.............................................................................................................................................................................94
38.6 Privacy, Security and Ethics........................................................................................................................................................................94
38.7 Broadband Proliferation...............................................................................................................................................................................94
38.8 How Organisations Attempt to Influence Public Policy................................................................................................................................94
38.9 Government Intervention.............................................................................................................................................................................94
38.10 Notable Examples of Government Policy and Intervention for Societal Benefit........................................................................................95
38.11 References................................................................................................................................................................................................95
39 Descriptive Capsil..........................................................................................................................................................................................................96
39.1 Issues..........................................................................................................................................................................................................96
39.2 Justification..................................................................................................................................................................................................96
39.3 Scientific (Basis/efficacy/evidence).............................................................................................................................................................96
39.4 Research.....................................................................................................................................................................................................96
39.5 Commercial.................................................................................................................................................................................................96
39.6 Standards....................................................................................................................................................................................................96
39.7 Gaps............................................................................................................................................................................................................96
39.8 Future Vision...............................................................................................................................................................................................96
40 Linking Capsil................................................................................................................................................................................................................97
41 White Papers..................................................................................................................................................................................................................98
41.1 EU White Papers.........................................................................................................................................................................................98
41.2 US White Papers.........................................................................................................................................................................................98
41.3 Japan White Papers....................................................................................................................................................................................98
42 ICT and Ageing Deployments.......................................................................................................................................................................................99
42.1 Retrofits.......................................................................................................................................................................................................99
42.2 New Builds...................................................................................................................................................................................................99
43 Workshops, Conferences and Portals.......................................................................................................................................................................100
43.1 Conferences..............................................................................................................................................................................................100
43.2 Portals.......................................................................................................................................................................................................101
43.3 Blogs..........................................................................................................................................................................................................102
44 Journals and Books....................................................................................................................................................................................................103
44.1 Journals.....................................................................................................................................................................................................103
44.2 Books.........................................................................................................................................................................................................103
45 Research Groups and Consortia................................................................................................................................................................................104
46 Initiatives and Funding................................................................................................................................................................................................105
46.1 International Initiatives in Aging.................................................................................................................................................................105
46.2 Interest Groups..........................................................................................................................................................................................105
46.3 Funding......................................................................................................................................................................................................106
46.4 Research Center or Projects.....................................................................................................................................................................106
47 Other Websites and Blogs..........................................................................................................................................................................................108
48 Events...........................................................................................................................................................................................................................109
49 Tom's Story..................................................................................................................................................................................................................110
49.1 Tom Capsil?s New Life..............................................................................................................................................................................110
49.2 Gaps..........................................................................................................................................................................................................111
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Table of Contents
50 Sean's Story.................................................................................................................................................................................................................112
50.1 Morning......................................................................................................................................................................................................112
50.2 Afternoon...................................................................................................................................................................................................112
50.3 Evening......................................................................................................................................................................................................113
50.4 Key Themes..............................................................................................................................................................................................113
50.5 GAP Analysis.............................................................................................................................................................................................113
51 Anna's Story.................................................................................................................................................................................................................114
51.1 Gaps..........................................................................................................................................................................................................114
52 Mitsuko and Setsuko's Story......................................................................................................................................................................................115
52.1 Gaps..........................................................................................................................................................................................................116
53 Jackie's Story...............................................................................................................................................................................................................117
53.1 Gaps..........................................................................................................................................................................................................117
54 Gap Analysis................................................................................................................................................................................................................118
54.1 Gaps in Research......................................................................................................................................................................................118
54.2 Gaps & Possibilities - Initial Strategy.........................................................................................................................................................118
55 Aarons Sandbox..........................................................................................................................................................................................................119
56 Chip? Antenna.............................................................................................................................................................................................................120
57 Accsense......................................................................................................................................................................................................................121
57.1 Accsense...................................................................................................................................................................................................121
57.2 Hardware Specifications (A1-01 General Purpose Pod)...........................................................................................................................121
57.3 Applications...............................................................................................................................................................................................121
57.4 Power........................................................................................................................................................................................................121
57.5 Software....................................................................................................................................................................................................121
57.6 Additional Information................................................................................................................................................................................121
58 Activities of daily living...............................................................................................................................................................................................122
59 ALARM-Net...................................................................................................................................................................................................................123
59.1 Privacy.......................................................................................................................................................................................................123
59.2 Security......................................................................................................................................................................................................124
60 Algorithms....................................................................................................................................................................................................................125
60.1 On-node Data Processing.........................................................................................................................................................................125
60.2 Sensor Fusion...........................................................................................................................................................................................125
60.3 Context Aware and Autonomic Sensing....................................................................................................................................................125
60.4 Data Mining and Trend Analysis................................................................................................................................................................125
60.5 Falls Detection Algorithms.........................................................................................................................................................................125
61 ANT...............................................................................................................................................................................................................................126
61.1 Ant.............................................................................................................................................................................................................126
61.2 Hardware Specifications............................................................................................................................................................................126
61.3 Applications...............................................................................................................................................................................................126
61.4 Power........................................................................................................................................................................................................126
61.5 Software....................................................................................................................................................................................................126
61.7 Papers.......................................................................................................................................................................................................126
62 Asbestos.......................................................................................................................................................................................................................128
62.1 Asbestos Policy in the UK.........................................................................................................................................................................128
63 References..................................................................................................................................................................................................................129
64 Atlas..............................................................................................................................................................................................................................130
64.1 Atlas...........................................................................................................................................................................................................130
64.3 Applications...............................................................................................................................................................................................130
64.4 Power........................................................................................................................................................................................................130
64.5 Software....................................................................................................................................................................................................130
64.7 Papers.......................................................................................................................................................................................................131
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65 Automatic wearable fall detectors.............................................................................................................................................................................132
65.1 Characteristics...........................................................................................................................................................................................132
65.2 Commerical Solutions................................................................................................................................................................................132
65.3 References................................................................................................................................................................................................132
66 Berg Balance Scale (BBS)..........................................................................................................................................................................................133
66.1 References................................................................................................................................................................................................133
67 Biocompatability..........................................................................................................................................................................................................134
68 Biotex Project...............................................................................................................................................................................................................135
69 Books............................................................................................................................................................................................................................136
69.1 Bulusu, Nirupama and Jha, Sanjay. WIRELESS SENSOR NETWORKS: A Systems Perspective, Artech House, Norwood, MA,
August 2005......................................................................................................................................................................................................136
69.2 Zhao, F. and Guibas, L. Wireless Sensor Networks: An Information Processing Approach, Morgan Kaufman, 2004..............................136
69.3 Yang, G.Z. Body Sensor Networks, Springer-Verlag London 2006...........................................................................................................136
69.4 Terrance J. Dishongh, Michael McGrath and Ben Kuris, Wireless Sensor Networks for Healthcare Applications, Artech House,
Boston 2008......................................................................................................................................................................................................137
70 Broadband Proliferation..............................................................................................................................................................................................138
71 Broadband Proliferation.............................................................................................................................................................................................139
72 BSN Node.....................................................................................................................................................................................................................140
72.1 BSN Node v3.............................................................................................................................................................................................140
72.3 Applications...............................................................................................................................................................................................140
72.4 Power........................................................................................................................................................................................................140
72.5 Software....................................................................................................................................................................................................140
72.7 Papers.......................................................................................................................................................................................................140
73 BTnode rev3.................................................................................................................................................................................................................141
73.1 BTnode Rev3.............................................................................................................................................................................................141
73.3 Applications...............................................................................................................................................................................................141
73.4 Power........................................................................................................................................................................................................141
73.5 Software....................................................................................................................................................................................................142
73.7 Papers.......................................................................................................................................................................................................142
74 Bulgaria........................................................................................................................................................................................................................143
74.1 Reimbursement Model in Bulgaria............................................................................................................................................................143
75 California......................................................................................................................................................................................................................144
75.1 California Reimbursement Model..............................................................................................................................................................144
76 CardioNET MCOT three lead ECG monitoring system.............................................................................................................................................145
77 Cardionetics C.Net 5000..............................................................................................................................................................................................146
78 Cardionetics C.Net5000 - 24-Hour Ambulatory ECG Monitor with Instant Analysis.............................................................................................147
79 Chipcon CC1000..........................................................................................................................................................................................................148
79.1 Chipcon CC1000.......................................................................................................................................................................................148
79.2 Applications...............................................................................................................................................................................................148
79.3 Features....................................................................................................................................................................................................148
79.4 Interfacing..................................................................................................................................................................................................148
79.5 Configuration.............................................................................................................................................................................................148
79.6 Transmission.............................................................................................................................................................................................148
79.7 Currently Used In.......................................................................................................................................................................................149
79.8 References and Additional Information.....................................................................................................................................................149
80 CODA............................................................................................................................................................................................................................150
81 CodeBlue......................................................................................................................................................................................................................151
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82 Communications..........................................................................................................................................................................................................153
83 Comparison of the internetwroking technologies....................................................................................................................................................154
84 Context Aware and Autonomic Sensing...................................................................................................................................................................155
84.1 Autonomic Sensing....................................................................................................................................................................................155
84.2 Collaborative Information Processing........................................................................................................................................................156
85 CONTEXT Project - Contactless Sensors for Body Monitoring Incorporated in Textiles.....................................................................................157
86 Cost and Reimbursement...........................................................................................................................................................................................159
87 References..................................................................................................................................................................................................................160
88 CRICKET.......................................................................................................................................................................................................................161
88.1 Cricket V2..................................................................................................................................................................................................161
88.3 Applications...............................................................................................................................................................................................161
88.4 Power........................................................................................................................................................................................................161
88.5 Software....................................................................................................................................................................................................161
88.7 Papers.......................................................................................................................................................................................................161
89 Data Mining and Trend Analysis................................................................................................................................................................................163
89.1 Data-Preprocessing...................................................................................................................................................................................163
89.2 Data Visualisation......................................................................................................................................................................................163
89.3 Descriptive Modelling and Clustering........................................................................................................................................................163
89.4 Probabilistic distributions...........................................................................................................................................................................163
89.5 Clustering..................................................................................................................................................................................................164
89.6 Predictive Modelling..................................................................................................................................................................................164
89.7 Pattern Mining...........................................................................................................................................................................................164
90 Denial of Service Attacks............................................................................................................................................................................................165
91 Denmark.......................................................................................................................................................................................................................166
91.1 Reimbursement Model in Denmark...........................................................................................................................................................166
92 Design Aspects of Body Sensor Networks...............................................................................................................................................................167
92.1 Overview....................................................................................................................................................................................................167
93 Key Design Considerations.......................................................................................................................................................................................168
93.1 Useability...................................................................................................................................................................................................168
93.2 Reliability and Stability...............................................................................................................................................................................168
93.3 Security of Sensor Networks.....................................................................................................................................................................168
93.4 Biocompatability........................................................................................................................................................................................169
93.5 RF Emissions and Interference Aspects...................................................................................................................................................169
93.6 Privacy & Security.....................................................................................................................................................................................169
93.7 Not Everyone Likes this Technology.........................................................................................................................................................170
93.8 Examples of Body Sensor Networks Concerned with Design Aspects.....................................................................................................170
93.9 Related EU Projects..................................................................................................................................................................................170
93.10 Summary and Recommendations For Further Work...............................................................................................................................171
93.11 References..............................................................................................................................................................................................171
94 Development Environments.......................................................................................................................................................................................172
95 DSY25...........................................................................................................................................................................................................................173
95.1 DSYS25.....................................................................................................................................................................................................173
95.3 Application.................................................................................................................................................................................................173
95.4 Power........................................................................................................................................................................................................173
95.5 Software....................................................................................................................................................................................................173
96 DSYS25.........................................................................................................................................................................................................................174
96.1 DSYS25.....................................................................................................................................................................................................174
96.3 Application.................................................................................................................................................................................................174
96.4 Power........................................................................................................................................................................................................174
96.5 Software....................................................................................................................................................................................................174
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96 DSYS25
96.7 Papers.......................................................................................................................................................................................................174
97 Eavesdropping.............................................................................................................................................................................................................175
98 ECG...............................................................................................................................................................................................................................176
99 Eco: Ultra-Wearable and Expandable Wireless Sensor Platform............................................................................................................................177
100 Ember..........................................................................................................................................................................................................................179
101 Ember.........................................................................................................................................................................................................................180
101.1 Hardware Specifications..........................................................................................................................................................................180
101.2 EM250.....................................................................................................................................................................................................180
101.3 EM260.....................................................................................................................................................................................................180
101.4 Application...............................................................................................................................................................................................181
101.5 Power......................................................................................................................................................................................................181
101.6 Software..................................................................................................................................................................................................181
101.7 Additional Information..............................................................................................................................................................................181
101.8 Papers.....................................................................................................................................................................................................181
102 EndoSure....................................................................................................................................................................................................................182
103 EnOcean.....................................................................................................................................................................................................................183
103.1 EnOcean..................................................................................................................................................................................................183
103.3 Application...............................................................................................................................................................................................183
103.4 Power......................................................................................................................................................................................................183
103.5 Software..................................................................................................................................................................................................183
103.7 Papers.....................................................................................................................................................................................................183
104 European Reimbursement Situation........................................................................................................................................................................184
105 References................................................................................................................................................................................................................185
106 Examples of Systems Demonstrating Biocompatability........................................................................................................................................186
107 References................................................................................................................................................................................................................187
108 Examples of Systems Designed with Privacy & Security In Mind........................................................................................................................188
109 References................................................................................................................................................................................................................189
110 Examples of Systems Designed with Security and Reliability In Mind................................................................................................................190
111 References................................................................................................................................................................................................................191
112 EyesIFXv1...................................................................................................................................................................................................................192
112.1 EyesIFXv1...............................................................................................................................................................................................192
112.3 Application...............................................................................................................................................................................................192
112.4 Power......................................................................................................................................................................................................192
112.5 Software..................................................................................................................................................................................................192
112.7 Papers.....................................................................................................................................................................................................192
113 EyesIFXv2...................................................................................................................................................................................................................193
113.1 EyesIFXv2...............................................................................................................................................................................................193
113.3 Application...............................................................................................................................................................................................193
113.4 Power......................................................................................................................................................................................................193
113.5 Software..................................................................................................................................................................................................193
113.7 Papers.....................................................................................................................................................................................................193
114 Finland........................................................................................................................................................................................................................194
114.1 Reimbursement Model in Finland............................................................................................................................................................194
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115 FireFly.........................................................................................................................................................................................................................195
115.1 FireFly......................................................................................................................................................................................................195
115.3 Applications.............................................................................................................................................................................................195
115.4 Power......................................................................................................................................................................................................195
115.5 Software..................................................................................................................................................................................................195
115.7 Papers.....................................................................................................................................................................................................195
116 FitSense BodyLAN....................................................................................................................................................................................................196
117 Fleck............................................................................................................................................................................................................................197
117.1 FleckTM 3................................................................................................................................................................................................197
117.3 Applications.............................................................................................................................................................................................197
117.4 Power......................................................................................................................................................................................................197
117.5 Software..................................................................................................................................................................................................198
117.7 Papers.....................................................................................................................................................................................................198
118 Floor Vibration-based fall detectors........................................................................................................................................................................199
118.1 References..............................................................................................................................................................................................199
119 Further detail on Healthcare Reimbursement in the USA......................................................................................................................................200
120 References................................................................................................................................................................................................................201
121 GaitRite.......................................................................................................................................................................................................................202
121.1 References..............................................................................................................................................................................................202
122 GE QuietCare system................................................................................................................................................................................................203
123 References................................................................................................................................................................................................................204
124 Germany.....................................................................................................................................................................................................................205
124.1 Reimbursement Model in Germany.........................................................................................................................................................205
125 Glacsweb....................................................................................................................................................................................................................206
125.1 Glacsweb.................................................................................................................................................................................................206
125.3 Applications.............................................................................................................................................................................................206
125.4 Power......................................................................................................................................................................................................206
125.5 Software..................................................................................................................................................................................................206
125.7 Papers.....................................................................................................................................................................................................206
126 Global Efforts Concerning Useability......................................................................................................................................................................208
126.1 References..............................................................................................................................................................................................208
127 HL7 Message..............................................................................................................................................................................................................209
128 Hoarder Board............................................................................................................................................................................................................210
128.1 Hoarder Board aka Swiss Army Knife (SAK)...........................................................................................................................................210
128.3 Applications.............................................................................................................................................................................................210
128.4 Power......................................................................................................................................................................................................210
128.5 Software..................................................................................................................................................................................................210
128.7 Papers.....................................................................................................................................................................................................211
129 How Organisations Attempt to Influence Public Policy.........................................................................................................................................212
130 Special Interest Groups and Non Governmental Organisations..........................................................................................................................213
131 Public Policy Research Organisations...................................................................................................................................................................214
132 Trade Associations and Labour Unions.................................................................................................................................................................215
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133 Individual Businesses..............................................................................................................................................................................................216
134 References................................................................................................................................................................................................................217
135 IMOTE2.......................................................................................................................................................................................................................218
135.1 IMOTE2...................................................................................................................................................................................................218
135.3 Applications.............................................................................................................................................................................................218
135.4 Power......................................................................................................................................................................................................218
135.5 Software..................................................................................................................................................................................................218
135.7 Papers.....................................................................................................................................................................................................219
136 Intel's HealthGuide....................................................................................................................................................................................................220
137 References................................................................................................................................................................................................................221
138 Ireland.........................................................................................................................................................................................................................222
138.1 Reimbursement Model in Ireland.............................................................................................................................................................222
139 IRIS..............................................................................................................................................................................................................................223
139.1 IRIS..........................................................................................................................................................................................................223
139.3 Applications.............................................................................................................................................................................................223
139.4 Power......................................................................................................................................................................................................223
139.5 Software..................................................................................................................................................................................................223
139.7 Papers.....................................................................................................................................................................................................223
140 IShoe...........................................................................................................................................................................................................................225
141 Journals......................................................................................................................................................................................................................226
141.1 Journals...................................................................................................................................................................................................226
141.2 Books.......................................................................................................................................................................................................226
142 Kiwok AB - BodyKom SeriesTM ECG - Kiwok AB..................................................................................................................................................227
143 Lifeguard....................................................................................................................................................................................................................228
144 L-Node........................................................................................................................................................................................................................229
144.1 L-Node (SOWNet Technologies platform)...............................................................................................................................................229
144.3 Application...............................................................................................................................................................................................229
144.4 Power......................................................................................................................................................................................................229
144.5 Software..................................................................................................................................................................................................229
144.7 Papers.....................................................................................................................................................................................................229
145 Low Power Antenna Design.....................................................................................................................................................................................230
145.1 Introduction..............................................................................................................................................................................................230
145.2 The Basic Antenna..................................................................................................................................................................................230
145.3 Antenna Characteristics..........................................................................................................................................................................230
145.4 Types of Antenna....................................................................................................................................................................................231
145.5 References..............................................................................................................................................................................................231
146 Medtronic Implantable Cardiovertor Defibrillator...................................................................................................................................................232
147 Memory.......................................................................................................................................................................................................................233
147.1 SD Cards.................................................................................................................................................................................................233
148 MICA2..........................................................................................................................................................................................................................234
148.1 MICA2......................................................................................................................................................................................................234
148.3 Applications.............................................................................................................................................................................................234
148.4 Power......................................................................................................................................................................................................234
148.5 Software..................................................................................................................................................................................................234
148.7 Papers.....................................................................................................................................................................................................235
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149 MICAz..........................................................................................................................................................................................................................236
149.1 MICAz......................................................................................................................................................................................................236
149.3 Applications.............................................................................................................................................................................................236
149.4 Power......................................................................................................................................................................................................236
149.5 Software..................................................................................................................................................................................................236
149.7 Papers.....................................................................................................................................................................................................237
150 Microcontroller...........................................................................................................................................................................................................238
150.1 Overview..................................................................................................................................................................................................238
150.2 MCU's......................................................................................................................................................................................................238
150.3 References..............................................................................................................................................................................................238
151 Minnesota...................................................................................................................................................................................................................239
151.1 Minnesota Reimbursement Model...........................................................................................................................................................239
152 Modified Dipole Antenna...........................................................................................................................................................................................240
153 MyHeart Project.........................................................................................................................................................................................................241
154 Nike and Ipod Rock and Run....................................................................................................................................................................................242
155 OFSETH - Optical Fibre Sesnors Embedded in to technical Textiles for Healthcare monitoring......................................................................243
156 On-node Data Processing.........................................................................................................................................................................................244
157 Other Websites..........................................................................................................................................................................................................245
158 Particles......................................................................................................................................................................................................................246
158.1 Particles...................................................................................................................................................................................................246
158.2 Core boards.............................................................................................................................................................................................246
158.4 Application...............................................................................................................................................................................................246
158.5 Power......................................................................................................................................................................................................247
158.6 Software..................................................................................................................................................................................................247
158.8 Papers.....................................................................................................................................................................................................247
159 Patient and environment assessment.....................................................................................................................................................................248
160 Overview....................................................................................................................................................................................................................249
161 References................................................................................................................................................................................................................251
162 Performance Oriented Balance and Mobility Assessment (POMA)......................................................................................................................252
162.1 References..............................................................................................................................................................................................252
163 PicoCricket.................................................................................................................................................................................................................253
163.1 PicoCricket..............................................................................................................................................................................................253
163.3 Applications.............................................................................................................................................................................................253
163.4 Power......................................................................................................................................................................................................253
163.5 Software..................................................................................................................................................................................................253
163.7 Papers.....................................................................................................................................................................................................253
164 Porcupine...................................................................................................................................................................................................................254
164.1 Porcupine 2v5..........................................................................................................................................................................................254
164.3 Applications.............................................................................................................................................................................................254
164.4 Power......................................................................................................................................................................................................254
164.5 Software..................................................................................................................................................................................................254
164.7 Papers.....................................................................................................................................................................................................255
165 Power Supply.............................................................................................................................................................................................................256
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166 Printed Circuit Whip, or ?Stub?...............................................................................................................................................................................257
167 Privacy........................................................................................................................................................................................................................258
168 Privacy and Ethics.....................................................................................................................................................................................................259
169 Privacy Concerns of Wireless Sensor Networks....................................................................................................................................................260
170 Privacy, Security and Ethics.....................................................................................................................................................................................261
171 Overview....................................................................................................................................................................................................................262
171.1 Situation in Europe..................................................................................................................................................................................262
171.2 Situation in the USA................................................................................................................................................................................262
172 References................................................................................................................................................................................................................263
173 Project STELLA - Stretchable Electronics for Large Area Applications..............................................................................................................264
174 Publications...............................................................................................................................................................................................................265
175 Radio Transceiver......................................................................................................................................................................................................266
175.1 Radio's.....................................................................................................................................................................................................266
175.2 Radio Chipsets........................................................................................................................................................................................267
175.3 References..............................................................................................................................................................................................267
176 Reliability....................................................................................................................................................................................................................268
177 Reliability and Stability.............................................................................................................................................................................................269
178 Reports.......................................................................................................................................................................................................................270
178.1 P. van der Stok, WASP Deliverable D1.2, State of the art, Information society technologies, March 2007............................................270
179 RF and Body effects..................................................................................................................................................................................................271
180 RF Emissions and Interference Aspects.................................................................................................................................................................272
181 References................................................................................................................................................................................................................273
182 Risk factors................................................................................................................................................................................................................274
182.1 Catogories...............................................................................................................................................................................................274
182.2 Intrinsic and Extrinsic Factors..................................................................................................................................................................274
183 Semi-Loop..................................................................................................................................................................................................................275
184 Sensing.......................................................................................................................................................................................................................276
185 Sensing......................................................................................................................................................................................................................277
185.1 Physical Transducers..............................................................................................................................................................................277
185.2 References..............................................................................................................................................................................................277
186 Sensor Fusion............................................................................................................................................................................................................278
186.1 Direct Data Fusion...................................................................................................................................................................................278
186.2 Feature Level Fusion...............................................................................................................................................................................278
186.3 Distance Metrics and Clustering..............................................................................................................................................................278
186.4 Dimensionality Reduction........................................................................................................................................................................279
186.5 Feature Selection....................................................................................................................................................................................279
186.6 Decision Level Fusion.............................................................................................................................................................................279
187 Sensor interfaces.......................................................................................................................................................................................................280
187.1 Analog Interface......................................................................................................................................................................................280
187.2 Digital Interface........................................................................................................................................................................................280
188 SHIMMER....................................................................................................................................................................................................................281
188.1 SHIMMER Wireless Sensor Platform......................................................................................................................................................281
188.3 Application...............................................................................................................................................................................................283
188.4 Power......................................................................................................................................................................................................283
188.5 Software..................................................................................................................................................................................................283
188.7 Papers.....................................................................................................................................................................................................283
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189 SHIMMER Wireless Sensor Platform.......................................................................................................................................................................284
189.1 SHIMMER Wireless Sensor Platform......................................................................................................................................................284
189.3 Application...............................................................................................................................................................................................286
189.4 Power......................................................................................................................................................................................................286
190 Sweden.......................................................................................................................................................................................................................287
190.1 Reimbursement Model in Sweden...........................................................................................................................................................287
191 TELOSB......................................................................................................................................................................................................................288
191.1 TELOSB\T-Mote\Sky-Mote......................................................................................................................................................................288
191.3 Application...............................................................................................................................................................................................288
191.4 Power......................................................................................................................................................................................................288
191.5 Software..................................................................................................................................................................................................288
191.7 Papers.....................................................................................................................................................................................................288
192 TMote Sky...................................................................................................................................................................................................................289
192.1 TELOSB/TMote Sky................................................................................................................................................................................289
192.3 Application...............................................................................................................................................................................................289
192.4 Power......................................................................................................................................................................................................289
192.5 Software..................................................................................................................................................................................................289
192.7 Papers.....................................................................................................................................................................................................289
193 Sky Mote.....................................................................................................................................................................................................................291
193.1 TELOSB\T-Mote\Sky-Mote......................................................................................................................................................................291
193.3 Application...............................................................................................................................................................................................291
193.4 Power......................................................................................................................................................................................................291
193.5 Software..................................................................................................................................................................................................291
193.7 Papers.....................................................................................................................................................................................................291
194 Texas...........................................................................................................................................................................................................................292
194.1 Texas Reimbursement Model..................................................................................................................................................................292
195 The Helical (Coil)........................................................................................................................................................................................................293
196 The Loop.....................................................................................................................................................................................................................294
197 The Patch....................................................................................................................................................................................................................295
198 The Short PCB Stub..................................................................................................................................................................................................297
199 The Short Whip..........................................................................................................................................................................................................298
200 The Slot.......................................................................................................................................................................................................................299
201 The Spiral...................................................................................................................................................................................................................300
202 The structure of EMRs and EHRs and the boundaries of each.............................................................................................................................301
203 The Timed Up and Go Test.......................................................................................................................................................................................302
204 The Underpants that Could Save your Life.............................................................................................................................................................303
205 The Veterans Administration....................................................................................................................................................................................304
206 References................................................................................................................................................................................................................305
207 TI CC2420...................................................................................................................................................................................................................306
207.1 TI CC2420...............................................................................................................................................................................................306
207.2 Applications.............................................................................................................................................................................................306
207.3 Features..................................................................................................................................................................................................306
207.4 Interfacing................................................................................................................................................................................................306
207.5 Configuration...........................................................................................................................................................................................306
207.6 Currently Used In.....................................................................................................................................................................................306
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207 TI CC2420
207.7 References and Additional Information...................................................................................................................................................307
208 T-Node........................................................................................................................................................................................................................308
208.1 T-Node (SOWNet Technologies platform)...............................................................................................................................................308
208.3 Application...............................................................................................................................................................................................308
208.4 Power......................................................................................................................................................................................................308
208.5 Software..................................................................................................................................................................................................309
208.7 Papers.....................................................................................................................................................................................................309
209 TRIL Falls Gait Analysis Platform............................................................................................................................................................................310
210 TRIL Gait Analysis Platform.....................................................................................................................................................................................311
211 Tunstall Fall Sensor...................................................................................................................................................................................................312
212 UK................................................................................................................................................................................................................................313
212.1 Reimbursement Model in UK...................................................................................................................................................................313
213 UK Government Intervention....................................................................................................................................................................................314
214 References................................................................................................................................................................................................................315
215 Useability....................................................................................................................................................................................................................316
216 User-activated alarms and pendants.......................................................................................................................................................................317
216.1 Justification..............................................................................................................................................................................................317
216.2 Research.................................................................................................................................................................................................317
216.3 Funding....................................................................................................................................................................................................317
216.4 Commercial.............................................................................................................................................................................................317
216.5 Standards................................................................................................................................................................................................317
216.6 Gaps........................................................................................................................................................................................................317
216.7 Future Vision...........................................................................................................................................................................................318
216.8 References..............................................................................................................................................................................................318
217 Vibering......................................................................................................................................................................................................................319
218 Video monitoring-based fall detectors....................................................................................................................................................................320
218.1 Introduction..............................................................................................................................................................................................320
218.2 Research Projects...................................................................................................................................................................................320
218.3 References..............................................................................................................................................................................................320
219 Viterion TeleHealth Network.....................................................................................................................................................................................321
220 References................................................................................................................................................................................................................322
221 Vivometrics Lifeshirt.................................................................................................................................................................................................323
222 WEALTHY Project - WEARABLE HEALTH CARE SYSTEM FOR VITAL SIGNS MONITORING...........................................................................324
223 WEALTHY Project - Wearable Healthcare Systems for vital signs monitoring...................................................................................................325
224 WEALTHY Project - Wearble Healthcare Systems for vital signs monitoiring....................................................................................................327
225 WEALTHY Project - Wearble Healthcare Systems for Vital Signs Monitoring.....................................................................................................328
226 WeBee.........................................................................................................................................................................................................................330
226.1 WeBee.....................................................................................................................................................................................................330
226.3 Application...............................................................................................................................................................................................330
226.4 Power......................................................................................................................................................................................................330
226.5 Software..................................................................................................................................................................................................330
226.7 Papers.....................................................................................................................................................................................................330
227 WeeBee.......................................................................................................................................................................................................................331
227.1 WeBee.....................................................................................................................................................................................................331
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227 WeeBee
227.3
227.4
227.5
227.6
227.7
Application...............................................................................................................................................................................................331
Power......................................................................................................................................................................................................331
Software..................................................................................................................................................................................................331
Additional Information..............................................................................................................................................................................331
Papers.....................................................................................................................................................................................................331
228 Wilomena and Will's Story........................................................................................................................................................................................332
229 Wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation......................................................333
230 Wireless Emissions and EMC...................................................................................................................................................................................334
xv
1 CAPSIL Overview
CAPSIL is an EU "Support Action" funded within the specific programme "Cooperation" and the research theme "ICT" of the 7th European Framework
Programme.
Our goals are to develop a detailed CAPSIL Roadmap for EU research to achieve effective and sustainable solutions to independent living. This
involves a baseline analysis of EU, USA and Japan followed by a period of visioning, gap analysis and documenting implementation details. We also
aim to produce this CAPSIL Wiki of knowledge blocks. This Wiki provides a support for aging research on the diverse problems and solutions available.
Finally, we would like to use our Wiki and roadmap to help policy makers in the EU, US and Japan coordinate research agendas and funding efforts.
This website represents a catalogue of solutions in the form of WiKi entries (CAPSILs) which describe interoperable ICT solutions to clinical
requirements for Independant Living that can then be deployed throughout the EU, US, and Japan for verification of the systems and testing of clinical
hypothesis of new and proposed research programmes. Each CAPSIL can act as an instructive entry point for a range of interested parties in
independent living research, development and deployment. For more details on the CAPSIL project visit our website or CAPSIL Overview.
These CAPSILs will enable clinicians and other care-givers to get the information they need to quickly and easily test solutions for prolonging
independent living within the many and various heterogeneous communities throughout the EU, the US, and Japan. The CAPSIL?s will be moderated
by an international collection of both ICT and clinical researchers initially composed of members of the CAPSIL team, but eventually expanding well
beyond.
1.1 CAPSIL Meetings
In practice along with use of internal collaboration tools, phones calls and exchanges we have many CAPSIL meetings. The CAPSIL meetings are to
coordinate the efforts within the CAPSIL team, to solicit input from experts outside CAPSIL, and to disseminate this information throughout the
community of researchers. The management workpackage (CAPSIL_MGMT WP1) will organize the four CAPSIL workshops, three of which will be held
alongside already existing international conferences.
The meetings include:
1. CAPSIL Meeting London, UK - April 10th 2008
2. CAPSIL Meeting Tokyo, Japan - July 30th - 31st 2008
3. CAPSIL Meeting EU Lyon - Nov 25th - 27th 2008
4. CAPSIL Meeting US Washington DC - March 18th - 20th 2009
5. CAPSIL Meeting other
1.2 CAPSIL Members
1. (Coordinator) University College, Dublin UCD Republic of Ireland
2. Intel Performance Learning Solutions Limited
3. Queens University, Belfast QUB United Kingdom
4. University of Genova UGDIST Ialty
5. Oregon Health Sciences University OHSU USA
6. Waseda University, Tokyo WUT Japan
7. Imperial College, London ICL United Kingdom
8. Spaulding Rehabilitation Hospital Corporation, Harvard Medical School, USA
1.3 CAPSIL Links
• [Website http://www.capsil.org]
1.4 CAPSIL Executive Summary
The aging of society is the single most important aspect of health care in the 21st century. Many intriguing ICT solutions are being developed within the
EU, USA, and Japan for helping older people remain independent longer. However, these solutions tend to be fragmented and heterogeneous. The
CAPSIL Coordinating Support Action (CSA) team is a strategic international coalition of University and Industrial partners that already have extensive
teams developing hardware/software/knowledge solutions to independent living based on user requirements. All partners of CAPSIL are already
members of regional and national centres on aging engaged in the process of helping to establish public policy and international standards. This support
action is to launch initiatives, coordinated and disseminated by a series of workshops in the US, EU, and Japan (two per year for two years), with three
fundamental goals:
• to develop a detailed CAPSIL Roadmap for EU research to achieve effective and sustainable solutions to independent living based on an
in-depth analysis of independent living requirements and the ICT scenarios developed or under development in the EU, as well as the US and
Japan (societies where the aging of the population are currently on par or exceeding the challenges that will be found within the EU).
• to support aging research by proposing procedures to incorporate all of these diverse solutions into WiKi entries (CAPSIL WiKi). These
CAPSILs will enable researchers and the ICT industry to get the information they need to quickly and easily test solutions for prolonging
independent living within the many and various heterogeneous communities. Only with this knowledge will the relevance and efficacy of
technological solutions be maintained and be empowered with the capability to be adapted for various cultures.
1
• to use the CAPSIL Roadmap and the CAPSIL Workshops to help policy makers in the US and Japan coordinate research agendas and
funding efforts across the three continents.
2
2 CAPSIL Meeting London, UK
2.1 Objectives (as per project Anex 1)
1. Presentation of international clinically-driven ICT solutions to aging ? results from CAPSIL work package support work
2. Identification of international conferences to hold next three workshops
3. Establishment of research support roadmap and determination of policy stakeholders that will incorporate the roadmap into orginiaztional
policy.
4. Definitions and structure of CAPSIL modules including outcome of investigation of CAPSIL?s as a social network platform such as
EU-PRACTICE.eu.
5. Establishment of a CAPSIL business plan sustain the CAPSIL infrastructure beyond the end of the project.
2.2 Working Meeting Part 1
• 9:00 - 9:30 Arrive and Network
• 9:30 ? 9:45 am Coffee (Group)
• 9.45 - 10:30 am CAPSIL Overview - CAPSIL Strategy (Group)
♦ (10 mins ) Strategy Ben Knapp (Queens)
♦ (30 mins) Introductions from the 8 partners, the PI introduces the partners and the role/value.
2.3 CAPSIL General Assembly
Note: The General Assembly is the decision-making body of the Consortium.
• 10:30-11:00 CAPSIL General Assembly (one voting member per partner) Agenda
♦ The General Assembly will choose four representatives to serve on the Executive Committee who will meet monthly in a virtual
roundtable meeting
♦ The General Assembly will choose an editor for the monthly e-zine, CAPSIL-news to work with Management Support Team
♦ The General Assembly will vote on the Management Support Team proposed by the CAPSIL coordinator
♦ The General Assembly will discuss CAPSIL representation at the FP7 consultation by ICT for Inclusion on 28/29 April 2008,
Brussels
2.4 Working Meeting Part 2
• 11.00 - 11.45 Session 1 Body Sensor Networks (INTC, Harvard, Imperial, QUB, UCD) Intervention Systems (OHSU WUT QUB DIST)
• 11.45 - 12:15 Sync 1 (15 Mins each session) (Group)
• 12.15 - 1:00 Session 2 Software and Interaction (DIST, INTC, WUT, QUB, UCD) / Clinical and Ethnographic Requirements (OHSU, UCD,
Harvard) (In parallel)
• 1:00 ? 1:30 Sync 2 (15 Mins each session) (Group)
• 1:30 - 2:00 Lunch
• 2:00 ? 2:45 Session 3 Home and Mobile Systems (OHSU, INTC, HVD) / CAPSIL Strategy (QUB, UCD, DIST, INTC, WUT) (In parallel)
• 2.45 - 3.15 Sync 3 (15 Mins each session) (Group)
• 3:15 ? 3:30 Coffee
• 3.30 - 5:00 Group Discussion CAPSIL Strategy (Group) and discussion on format/tools for documenting (CAPSIL WiKi entries) (eg. Gait
Analysis)
• 5:00-5:30 Closing and Next Meeting objectives: Coordinator (Group)
2.5 Directions to the campus/ meeting venue
The meeting took place at the Institute of Biomedical Engineering, meeting room 1 and 2, South Kensington Campus, Imperial College London. Meeting
rooms 1 and 2 are located on level 4 of the Institute of Biomedical Engineering, Bessemer building.
3
3 CAPSIL Meeting Tokyo, Japan
The CAPSIL consortium members gathered in Tokyo for our second CAPSIL Workshop in Waseda University on July 30/31. Each CAPSIL meeting had
an increasingly expansive set of objectives in order to involve the entire independent living community from all three regions (US, Japan, and EU), in the
process of developing our roadmap and instantiating CAPSILs.
Professor Hashimoto was instrumental in the planning and coordination for this event. Included we had a visit to the Waseda University Joint Institution
for Advanced Biomedical Sciences along with policy, funding and independant living research presentations from, Prof. Toshiyo Tamura (Chiba
University), Mr. Dai Hiyama (Yamatake Co.), Mr. Tatsuya Yamazaki (NICT), Mr. Kunihiko Niwa (JST/CRDS). During this two day workshop the
members of the consortium updated each other on their progress with instantiating CAPSILs, knowledge dissimination and general roadmap work.
3.1 CAPSIL Contract Objectives
1. Presentations of Japanese aging and independent living research agenda by CAPSIL Japanese partner.
2. Presentations of EU research and policy on aging and independent living research agenda by CAPSIL EU partner.
3. Presentation and open discussion of research support roadmap
4. Presentation of international clinically-driven ICT solutions to aging ? results
5. Internal Workshop on CAPSIL Roadmap
6. Open Workshop on CAPSIL Roadmap
7. Internal Workshop on CAPSIL modules
8. External Workshop on CAPSIL modules including discussions of contributors and moderators.
4
4 CAPSIL Meeting EU
The Lyon CAPSIL General Assembly meeting was co-located with ICT 2008. The conference itself ran from 25th-27th November. We held the Capsil
Consortium meeting from 09:00-19:00 on the 28th and 09:00-14:00 on the 29th. It took place in INSA Lyon in the Marco Polo Room on the 1st floor of
the Marco Polo Building. In addition to reports from the team on roadmap, usage models, gap analysis and support activities we had presentations from
a number of leading European figures on the challenges for Independent Living research going forward.
5
5 CAPSIL Meeting US
From the 18th to the 20th of March the CAPSIL consortium came together in Washington DC for our 4th General Assembly, 4th working session along
with a large program of events with US based funding and decision makers. These 3 days of events were hosted at the Eunice Kennedy Shriver
National Institute of Child Health and Human Development, National Institutes of Health at the U.S. Department of Health and Human Services in
Rockville Maryland.
The CAPSIL consortium held our 4th general assembly during this time along with further time devoted to roadmap development. Over the course of
three days CAPSIL hosted bilateral meetings between representatives from the EU with representatives from the US. Those from the EU included,
members of CAPSIL, representatives of the Delegation of the European Commission in Washington DC, representatives of the European Commission,
Information Society & Media D-G and representatives of the Ambient Assisted Living (AAL) Joint Programme. Those from the US for example included,
representatives of the National Institutes of Health (NIH), specifically the National Institute on Aging (NIA) and the National Institute of Child Health and
Human Development (NICHD). Further meetings with the NSF and the National Institute of Biomedical Imaging and Bioengineering happened during
this three day period.
The goal for these bilateral meetings is to further understand the activities underway in each area focussed on independent living supported by
technology and to strengthen the opportunities for EU and US researchers to collaborate. There are many funded and unfunded research and
development programs in the EU, US and Japan and if the agencies can aid, rely and build on each others efforts then the collective efforts of not just
one region but all can be brought to bear on the challenges in independent living.
5.1 CAPSIL Contract Objectives
1. Presentations of US aging and independent living research agenda by US CAPSIL partners.
2. Presentations of EU research and policy on aging and independent living research agenda by CAPSIL EU partner.
3. Presentation and open discussion of research support roadmap
4. Presentation of international clinically-driven ICT solutions to aging ? results from CAPSIL work packages
5. Internal Workshop on CAPSIL Roadmap
6. Open Workshop on CAPSIL Roadmap
7. Internal Workshop on CAPSIL modules
8. External Workshop on CAPSIL modules including discussions of contributors and moderators
6
6 CAPSIL Meeting other
6.1 Objectives
1. Finalization of CAPSIL Roadmap
2. Presentation of CAPSIL Roadmap
3. Presentation of final CAPSIL structure and initial CAPSIL solutions.
4. Establishment of on-going CAPSIL Scientific and Moderating Group.
7
7 Introduction to CAPSILs of knowledge
The CAPSILs of knowledge in this Wiki provide a catalogue of solutions in the form of WiKi entries (CAPSILs) which describe interoperable ICT
solutions to clinical requirements for independent living that can then be deployed throughout the EU, US, and Japan for verification of the systems and
testing of clinical hypothesis of new and proposed research programmes. These CAPSILs enable clinicians and other care-givers to get the information
they need to quickly and easily test solutions for prolonging independent living within the many and various heterogeneous communities throughout the
EU, the US, and Japan. The CAPSIL?s are intended to be moderated by an international collection of both ICT and clinical researchers initially
composed of members of the CAPSIL team, but eventually expanding well beyond.
Only with this kind of instrument will the cross-cultural relevance and efficacy of technological solutions be maintained. It is envisaged that in the initial
stages of the project that information will be ?pulled? into the project from CAPSIL partners to seed the CAPSILs. Active recruitment of the broader
community of CAPSIL users will then help contribute to a repository of knowledge accessible to all audiences. The CAPSIL website and associated
WIKI will be designed according to W3C/WAI/WCAG1.0 level AA.
High-level areas for our current CAPSILs include:
• Wellbeing & Disease
• Prevention & Intervention
• Technologies
• Other CAPSILs
• Types of CAPSIL
Return to Main Page from where you can find all the main CAPSILs linked.
8
8 COPD
General Description
Chronic obstructive pulmonary disease (COPD) is a progressive disease that makes it hard to breathe. There are two main forms of COPD: 1) chronic
bronchitis, which causes long-term swelling and a large amount of mucus in the main airways in the lungs, and 2) emphysema, a lung disease that
destroys the air sacs in the lungs. Most people with COPD have symptoms of both. Smoking is the leading cause of COPD. COPD afflicts more than 15
million Americans, results in more than 15 million physician office visits each year, and causes approximately 150 million days of disability per year [1].
The total direct cost of medical care related to COPD is approximately $15 billion per year [2]. Similar incidence is observed in Europe and Japan.
Worldwide 80 million people suffer from moderate to severe COPD and 3 million died due to it in 2005. Chronic obstructive pulmonary disease (COPD)
is the fourth leading cause of death, and is projected to rank fifth in 2020 in worldwide burden of disease.
Issues
COPD is a steadily progressive, debilitating disease for which existing medical therapies are largely ineffective. Clinically, patients experience stable
periods punctuated by exacerbations. Exacerbations are commonly defined to be episodes of increased dyspnea, cough, and change in amount and
character of sputum, resulting in a change in medical therapy. Exacerbations are thought to be due to infection or environmental exposure, leading to
airway inflammation. The frequency of exacerbations has been shown to be an important determinant of health-related quality of life (HRQL) in COPD
and contributes to long-term decline in lung function [3].
Early detection and treatment of an exacerbation in the outpatient setting are important to prevent worsening of clinical status and need for emergency
room care or hospital admission. Available preventative therapies to reduce exacerbation frequency, such as influenza vaccination, smoking cessation,
pulmonary rehabilitation, and long-term oxygen therapy, have been found to have only a relatively small effect [4]. Early treatment is associated with
faster recovery, better HRQL, and lower risks of emergency hospitalization. Therefore, early identification of exacerbations would improve the timing of
physician consultation, reduce exacerbation severity and disease progression, and reduce the burden of inpatient treatment of exacerbations on
healthcare services.
Demonstration of airflow limitation is essential for the diagnosis of COPD. Decline in forced expiratory volume in 1 sec (FEV1) over time has been used
as the ?gold standard? measure of disease progression in COPD. However, patients with COPD have systemic manifestations that are not reflected by
the FEV1. The FEV1 correlates weakly with the degree of dyspnea [5]. Prospective, observational studies of patients with COPD have found that the
degree of dyspnea and health status scores are more accurate predictors of the risk of death than is the FEV1 [6]. Standard pulmonary function testing
provides information on functional lung capacity at rest but limited information on ventilatory requirements or functional performance during physical
activity. It is often necessary to quantify the degree of exercise intolerance experienced by the patient with COPD. Importantly, it has been shown that
lung function does not decline at the onset of an exacerbation [7]. The first signs of exacerbation are deteriorations in the symptoms of dyspnea, sore
throat, cough, and symptoms of a common cold, but not lung function [8]. ?Alternative measures are needed that better reflect the clinical status of
patients with COPD and allow detection of clinically important responses to therapies [9]? states the Global Initiative for Chronic Obstructive Lung
Disease executive summary report.
Justification
Economic analyses have shown that over 70 % of COPD-related health care expenditures result from emergency room visits and hospital care for
exacerbations. This translates into > $10 billion annually in the US. Early identification of an exacerbation and prompt treatment improves recovery time,
reduces risks of emergency hospitalization, and is associated with better health-related quality of life. Thus, strategies for early detection of
exacerbations leading to early treatment in the outpatient setting have potential substantial clinical and economic benefit.
Research
Numerous studies are ongoing in the US, Europe and Japan to assess interventions and patient status monitoring methodologies. NIH recently
organized a workshop to identify areas in need for research to identify better interventions and monitoring methodologies in COPD and has made a
large effort to increase awareness of general public of research performed in this and other major areas of work of NIH.
Commercial
COPD management is largely based on pharmacological interventions. From a commercial standpoint, this is a market of significant size. The use of
combined inhaled corticosteroid and long-acting beta-agonist is a major part of COPD management. Long-acting muscarinic antagonist therapy has also
been a major area of research by pharmaceutical companies.
Spirometry is a key component of the tools available to clinicians to assess lung function in patients with COPD. Numerous products are available to
gather such measures. Extensive literature is available that addresses the use of spirometry in COPD. Pulse oxymetry is also commonly used in COPD
management often in combination with oxygen therapy.
Gaps
There is a need for early identification of COPD exacerbations. Lung function measures - such as FEV1 - are inadequate in describing the COPD
disease state. Exercise capacity is an important indicator of COPD status, but current methods of measuring exercise capacity are limited by their
assessment of the patient at a single point in time in a controlled laboratory environment and focus on lower extremity exercise. Measurement of
exercise in the home and outdoor environments may potentially provide an integrative, accurate and sensitive measure of COPD status (stable versus
exacerbation) [10-12].
9
Future Vision
Unobtrusive system of miniature sensors could be utilized for the detection of physical activities and measurement of associated physiological
responses in oxygen saturation, heart rate, and respiratory rate for early detection of exacerbations in patients with COPD. Compared to healthy
subjects, physical activity is significantly reduced in patients with moderate to severe COPD. Furthermore, among persons with COPD, higher levels of
exercise capacity and physical activity are associated with better outcomes and survival.
In parallel with the development of wearable systems, it is necessary to implement data analysis procedures to identify physical activities and assess
associated physiological responses. These methodologies would have to be tested in COPD patients during exacerbation episodes to study whether it is
possible to achieve early detection of an exacerbation. Further, associations should be sought between measures of physical activity and physiological
responses and existing laboratory-based, episodic measures of clinical status to explore the hypothesis that physical activity is a biomarker of COPD
status.
References
[1] Croxton TL, Weinmann GG, Senior RM, Wise RA, Crapo JD, Buist AS, ?Clinical research in chronic obstructive pulmonary disease: needs and
opportunities?, Am J Respir Crit Care Med, 167(8): 1142-1149, 2003.
[2] Sullivan SD, Ramsey SD, Lee TA, ?The Economic Burden of COPD?, Chest, 117: 5S?9S, 2000.
[3] Donaldson GC, Seemungal TAR, Bhowmik A, Wedzicha JA. Relationship between exacerbation frequency and lung function decline in chronic
obstructive pulmonary disease. Thorax 57:847-852, 2002
[4] Garcia-Aymerich J, Barreiro E, Farrero E, Marrades RM, Morera J, Anto JM, and the EFRAM Investigators. Patients hospitalized for COPD have a
high prevalence of modifiable risk factors for exacerbation (EFRAM study). Eur Respir J 16:1037-1042, 2000.
[5] Mahler DA, Weinberg DH, Wells CK, Feinstein AR, ?The measurement of dyspnea. Contents, interobserver agreement, and physiologic correlates of
two new clinical indexes?, Chest, 85(6): 751-758, Jun 1984.
[6] Domingo-Salvany A, Lamarca R, Ferrer M, Garcia-Aymerich J, Alonso J, Felez M, Khalaf A, Marrades RM, Monso E, Serra-Batlles J, Anto JM,
?Health-related quality of life and mortality in male patients with chronic obstructive pulmonary disease?, Am J Respir Crit Care Med,166(5):680-685,
Sept 2002.
[7] Seemungal TAR, Donaldson GC, Paul EA, Bestall JC, Jeffries DJ, Wedzicha JA. Effect of exacerbation on quality of life in patients with chronic
obstructive pulmonary disease. Am J Respir Crit Care Med 157:1418-1422, 1998.
[8] Seemungal TAR, Donaldson GC, Bhowmik A, Jeffries DJ, Wedzicha JA. Time course and recovery of exacerbations in patients with chronic
obstructive pulmonary disease. Am J Respir Crit Care Med 161:1608-1613, 2000.
[9] Connors AF, Dawson NV, Thomas C, et al. Outsomes following acute exacerbations of severe chronic obstructive lung disease. Am J Respir Crit
Care Med 154:959-967, 1996.
[10] Hecht A, Ma S, Porszasz J, Casaburi R, COPD Clinical Research Network. Methodology for using long-term accelerometry monitoring to describe
daily activity patterns in COPD. COPD. 6(2):121-9, 2009.
[11] Patel SA, Benzo RP, Slivka WA, Sciurba FC. Activity monitoring and energy expenditure in COPD patients: a validation study. COPD. 4(2):107-12,
2007.
[12] Coronado M, Janssens JP, de Muralt B, Terrier P, Schutz Y, Fitting JW. Walking activity measured by accelerometry during respiratory
rehabilitation. J Cardiopulm Rehabil. 23(5):357-64, 2003.
10
9 Congestive Heart Failure
General Description
Congestive Heart Failure (CHF), or heart failure, is a condition in which the heart can't pump enough blood to the body's other organs. Congestive Heart
Failure is the most frequent cause of disability and death in persons aged 65 years and older (20).
Remote physiological monitoring of CHF allows physiological indicators and symptoms to be observed and recorded by a carer or healthcare
professional without the need to be in the same physical location as the patient. This reduces some of the burden on the healthcare system and allows
for monitoring in the home.
Remote CHF monitoring systems examine one or more of the following indicators:
• Weight (Device: Weight Scales)
• Blood Pressure (Device: BP Monitor)
• Peak Expiratory Flow Rate (Device: Peak Flow Meter)
• Blood oxygen saturation (Device: Pulse Oximeter)
These indicators are recorded by an aggregator which records the data and forwards it on to the relevant carer or GP. This device may also act as an
actuator which feeds information back to the patient, such as medication reminders or advice from medical staff.
This CAPSIL entry point was added online during the CAPSIL Meeting EU.
9.1 Issues
While home or telehealth monitoring has been shown to have health and economic benefits, it is dependent on the patient adhering to the monitoring
regime. At present monitoring is done through explicit devices which the patient must be trained to use. This equipment is not yet cheap or universal and
may not integrate with other health information systems. There are also open questions about who has/should have access to different levels of patient
health information and where responsibility for responding to telehealth data lies.
9.2 Justification
Congestive Heart Failure is the most common cause of re-admission to hospitals, each costing up to $10 000 and is expected to double over the next 40
years (18). Patient adherence to medication is a key factor in successful treatment of CHF, often requiring long-term use of multiple medications to treat
the problem as well as further medication to ameliorate signs and symptoms. A major factor in re-admissions is failure to detect early indicators of
episodes and patient non-adherence to medication or treatment regimes. Changing the current focus on treatment following acute episodes or attacks
through preventative intervention systems and home based monitoring will reduce the need for expensive hospital admissions.
9.3 Scientific (Basis/efficacy/evidence)
It is estimated that in the U.S. telehealth services could reduce physician visits by 20% at a saving of up to $9 billion (15), with institutional care
representing the largest and most expensive component of CHF treatment (21). Research has shown that 53% of CHF related hospital readmissions
are preventable due to noncompliance with medication, diet or detection of problematic indicators (19). Poor adherence to medication is a contributing
factor in 20-64% of hospital readmissions for CHF related problems (22). According to (15) ?The ability to collect sound clinical data, combined with
improved communication between patients and nurses, increased patient compliance.?
Plant and Moran P.L.L.C. health care consulting preliminary studies have indicated that telemedicine does reduce hospital and ER visits and results in
better patient outcomes. Studies carried out by Pearson et al. in the University of South Australia, University of Queensland and the Chinese University
of Hong Kong have showed that home based intervention methods in patients with Chronic Illness have reduced hospital admission rates by 14% within
two years and by 21% in all surviving patients within 3 to 8 years. Research completed by Pat A. Heffernan, President Genesee Region Home Care &
Hospice showed that hospital admission was reduced by 28% using a daily home monitoring systems and resulting hospital re-admission reduced by
8% as a result. The department of Veteran Affairs has shown a 35% drop in hospital readmissions and a 60% drop in emergency visits for diabetic and
lung disease patients by deploying a remote monitoring tool for physically impaired patients at home. Strategic Healthcare Programs (SHP) have shown
that an average improvement rate in the stabilization in ADL?s of patients with CHF increased by 4.9% when monitored by a home monitoring device
and improvements of 10.9% in stabilization of independent activities of daily living (IADL) They also showed that hospitalization decreased by 3.9% in
monitored CHF patients.
Some studies indicate that patients who utilize in-home telehealth monitoring systems have a higher self-perceived quality of life (24). This may be due
to comfort/familiarity factors combined with not perceiving themselves as ?sick? due to less frequent physical contact with doctors and the health
services.
9.4 Research
9.4.1 Players
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9.4.1.1 US
The National Institute of Health (NIH) is the umbrella organisation in responsible for health funding in the United States. It has a number of
sub-organisations or Institutes with specific areas of responsibility, such as the Institute of Medicine (IOM) or the National Institute on Aging.
The American Heart Association is a non-profit organisation that promotes cardiac care and has programs funding research into heart health.
9.4.1.2 EU
European Public Health Alliance EPHA Site
EU Public Health Portal
In the UK the National Health Service (NHS) runs one of the largest health information systems in the world.
SHAPE The Study Group on Heart Failure Perception and Awareness in Europe was founded in 2002 by a group of independent medical specialists
with the aim of improving heart failure care across Europe through increasing public awareness.
9.4.1.3 Japan
Japan Public Health Association An incorporated foundation dedicated to promoting healthy living for Japanese citizens. Funds research into public
health, co-ordinates activities of public health organisations, dissemination of information, hosting academic conferences.
9.4.2 Projects
Home Health Monitor - University of Illinois at Chicago
The HHM is a hand held device with an integrated modem which automatically transmits the following physiologic indices directly to a computer server:
Blood pressure levels and pulse-rate (automatic, non-invasive), Weight, Blood oxygen saturation levels, Glucose levels
In a pilot study, the hospital readmission rate for 35 congestive heart failure patients using the Home Health Monitor was 14% over a six-month period,
compared to the national readmission average of 42% over a three-month period.
TeleWatch Patient Monitoring System - The John Hopkins University, Applied Physics Laboratory
The Home Link System provides for communication and data recording of patient information, such as information for monitoring and managing
congestive heart failure (CHF).
PulmoTrace@Home - Tel-Aviv University
CardioInspect employs the bio-impedance and electrical impedance tomography (EIT) principles in order to monitor and diagnose pulmonary edema
and cardiac functionality indices in CHF patients. The Tele-Medicine home system is designed for CHF patients for an on-going monitoring of lung
congestion at home environment, allowing the detection of potential CHF condition degradation. The measured data, including right and left lungs
resistivities and a 10-seconds ECG, is transmitted via modem-connection into a medical-center, which gathers all incoming data, and compares it to
previous measurements to help determine further treatment.
9.4.3 Funding
9.5 Commercial
9.5.1 Products
Honeywell HomMed System
A suite of hardware and software home monitoring solutions, that can measure heart rate, blood pressure, oxygen saturation, temperature and weight.
Also prompts users as to when it's time to measure their vital signs. Includes the Genesis, Sentry and Lifestream systems.
Medtronic Inc. Reveal Insertable Cardiac Monitor
An insertable cardiac monitor is a small implantable device that continuously monitors heart rhythms and records them automatically or by using a
hand-held patient activator. The device is implanted just beneath the skin in the upper chest area, during a simple procedure.
Telcomed
Wristclinic/Miniclinic - Portable and mobile multi-parameter telehealth monitoring system measuring: Heart rate, ECG, Blood pressure, Heart rhythm
12
regularity, Respiratory rate, Oxygen saturation (SpO2), Body temperature. Also manufacture and sell: Blood Pressure Monitor, Wireless Weight Scale
and the MedicGate/MiniGate vital signs collecting gateways for use in conjunction with their systems.
Cardiocom Telescale
Interactive networked weighing scales for monitoring of CHF. Also Compact Telescale
Cardiocom Commander
Cardionetics have ambulatory ECG halter monitors that can also preform analysis and abnormality detection and alerts.
Modular telehealth monitoring system that takes a variety of sensors and so can be configured for specific patient needs.
Docobo HealthHub PDA style interactive system capable of monitoring a variety of chronic diseases.
9.5.2 Players
Tunstall are the parent company of Honeywell and have interests in technologies for many aspects of ageing.
Medic4all
Cardiocom
Home Telehealth
Docobo
GE Healthcare
9.5.3 Procurement
9.5.4 Business Models
Home monitoring systems with the ability to track weight, temperature, peak flow, and blood pressure allows for the ability to respond quickly if vital
signs return abnormal results. This allows physicians to give priority to patients that have been ?flagged? and helps avoid hospital stays and strains on
resources .Strategic Healthcare Programs (SHP) have shown that an average improvement rate in the stabilization in ADL?s of patients with CHF
increased by 4.9% when monitored by a home monitoring device and improvements of 10.9% in stabilization of independent activities of daily living
(IADL) They also showed that hospitalization decreased by 3.9% in monitored CHF patients.
Business models for CHF measurement have a long history starting with the provision of holter monitors and cardiac event monitors for recording and
transmitting ECG outside the hospital environment. Generally these were reimbursed by the insurance or government health body, possibly after
approval by a primary care physician More recently the business models are driven by the telehealth.
Current business models are built around provision of home monitoring services to healthcare providers, provision of equipment and software,
communications networks and response to alerts.
Business models need to be developed which allow for cost effective integration of telehealth services into existing systems and services. Such services
may piggyback on existing services, exploiting synergies with them or develop new methods for recruiting clients. An excellent example of leveraging
existing technologies is the ?Heart Monitor? application (28) for Apples? iPhone. This is a simple software download, which lets the user monitor their
heart rate using their iPhone hardware. As yet this is not connected to any telehealth service but is a good example of cross-purposing consumer
technology.
Opportunities exist for companies specialized in data security for healthcare systems and should be promoted and exploited. Provision of services to
telehealth users: patient/disease tracking and care, treatment appointment reminders. An example of this type of service is the Canadian Ministry of
Healths? ?Chronic Disease Management Toolkit? (16). Such service providers must work to gain patient/customer confidence and provide tangible
benefits to the end-user, particularly when they are to work in conjunction with state healthcare systems.
Companies such as Nike have capitalised on customers competitive spirit by offering exercise tracking in some of their new products, allowing users to
graph their performance and compare it to that of their peers. We see this as an important approach to promote adherence to exercise and prevention
regimes.
9.6 Standards
Interoperability standards are key to the future success of telehealth and home care information systems. HL7 is a commonly adopted standard
supported by the American National Standards Institute (ANSI) and the International Organization for Standardization (ISO). HL7 develops Conceptual
standards, Document standards, Application standards and Messaging standards (particularly important for interoperability of systems and devices).
Some applicable standards are: ISO/TR 16056-1:2004 Health informatics -- Interoperability of telehealth systems and networks -- Part 1: Introduction
13
and definitions and ISO 11073-90101:2008 Health informatics -- Point-of-care medical device communication -- Analytical instruments -- Point-of-care
test
9.7 Gaps
9.7.1 Gaps in technology
Unobtrusive, low power, long life sensors are still not widely commercially available, at prices and standards that allow ubiquitous deployment.
Reliable and secure wireless communications are still not available at the standards required for medical use.
9.7.2 Gaps in the basic science
Most studies carried out into CHF have not looked at patients with multiple morbitities or symptoms, generally in order to simplify results. Little is known
about interactions between treatment regimes or adverse side effects on susceptible patients e.g. the controversy over Rosiglitazone
More research is needed into pattern recognition algorhythms for detection of CHF indicators via remote sensors or telehealth systems. Also work into
effective triaging from data supplied by telehealth or AAL systems is needed.
9.7.3 Gaps in operation
Many of the patient-worn CHF monitoring devices are too complex in use and operation. Also displays and buttons tend to be too small for easy use by
those with visual or physical impairments.
9.7.4 Gaps in implementation
Current systems are difficult to use for non-medically trained (home) users. Human Computer Interaction (HCI) design for older users in general has a
long way to go.
Some standards for interoperability exist but there is not yet a common agreement among manufacturers and customers as to 'definitive' or universal
standards.
Standards for reporting of health information (both critical and non-critical) to telehealth users, carers and healthcare workers are needed. Also clearly
defined systems and procedures for responding to medical alerts delivered as part of a telehealth system.
9.8 Future Vision
Multiple 'invisible' sensors deployed throughout an 'ambient assisted living' environment, able to monitor CHF relevant physiological factors, potentially
as part of a larger integrated system for home health, not requiring a regular measurement routine. System feedback when necessary (e.g. medication
reminders) or carer/clinician notification. Integration into non-physiological monitoring systems for diet, exercise, social connectedness.
9.9 Issues
Patient Compliance - patients must remember/want to take regular physiological measurements themselves and forward them to the relevant person.
This may create feelings of 'illness' or be inconvenient. Other barriers to adherence related to therapy include: adverse effects, polypharmacy, frequent
dosing and cost. Other reasons for nonadherence are: poor communication/education regarding the medication, complexity of drug regimens and failure
to initiate therapy in hospital when the patient is most likely to relate the drug to health (23).
Ease of use - the monitoring device must be usable by an elderly person, who may have some form of functional or cognitive impairment.
Interoperability of monitoring systems with healthcare IT systems - the monitoring device must provide data in the right format to be accessible to the
relevant healthcare professional
Data security - the data from the monitoring system must not be accessible to unauthorised users.
Sensor Reliability - the healthcare professional must be able to make accurate and reliable diagnosis from the data provided and able to detect system
failure.
Genetic screening may be of use in determining susceptibility to CHF (14), however there are ethical and social issues to be considered, such as who
has access to the data (employers, insurers, family, government).
Cost - the initial outlay makes current telehealth systems still relatively expensive, however it is believed that they will ultimately have cost savings by
avoiding costly emergency treatment. More research is needed into the efficacy of such systems in order to prove their worth to insurers and healthcare
providers.
14
9.10 References
1 Singh VN. Congestive Heart Failure. eMedicine.com. URL: http://www.emedicine.com/radio/topic189.htm. Accessed on April 14, 2006.
2 Erik B Friedrich MD & Michael B?hm MD (2006). Treatment of Chronic Heart Failure.
3 Krum H, National Heart Foundation of Australia and Cardiac Society of Australia & New Zealand Chronic Heart Failure Clinical Practice Guidelines
Writing Panel. (2001). "Guidelines for management of patients with chronic heart failure in Australia." Med J Aust 174 (9): 459-66. PMID 11386592.
4 Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, Jessup M, Konstam MA, Mancini DM, Michl K, Oates JA, Rahko PS, Silver
MA, Stevenson LW, Yancy CW, Antman EM, Smith SC Jr, Adams CD, Anderson JL, Faxon DP, Fuster V, Halperin JL, Hiratzka LF, Jacobs AK,
Nishimura R, Ornato JP, Page RL, Riegel B; American College of Cardiology; American Heart Association Task Force on Practice Guidelines; American
College of Chest Physicians; International Society for Heart and Lung Transplantation; Heart Rhythm Society. (2005). "ACC/AHA 2005 Guideline
Update for the Diagnosis and Management of Chronic Heart Failure in the Adult". Circulation 112 (12): e154-235. PMID 16160202.
5 Granger CB, McMurray JJ, Yusuf S, Held P, Michelson EL, Olofsson B, Ostergren J, Pfeffer MA, Swedberg K; CHARM Investigators and Committees.
(2003). "Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function intolerant to
angiotensin-converting-enzyme inhibitors: the CHARM-Alternative trial." Lancet 362 (9386): 772-6. PMID 13678870.
6 Pfeffer MA, Swedberg K, Granger CB, Held P, McMurray JJ, Michelson EL, Olofsson B, Ostergren J, Yusuf S, Pocock S; CHARM Investigators and
Committees. (2003). "Effects of candesartan on mortality and morbidity in patients with chronic heart failure: the CHARM-Overall programme." Lancet
362 (9386): 759-66. PMID 13678868.
7 Exner DV, Dries DL, Domanski MJ, Cohn JN (2001). "Lesser response to angiotensin-converting-enzyme inhibitor therapy in black as compared with
white patients with left ventricular dysfunction.". N Engl J Med. 344 (18): 1351-7. PMID 11333991.
8 Taylor AL, Ziesche S, Yancy C, Carson P, D'Agostino R Jr, Ferdinand K, Taylor M, Adams K, Sabolinski M, Worcel M, Cohn JN; African-American
Heart Failure Trial Investigators. (2004). "Combination of isosorbide dinitrate and hydralazine in blacks with heart failure." N Engl J Med 351 (20):
2049-57. PMID 15533851.
9 Erik B Friedrich MD & Michael B?hm MD (2006). Treatment of Chronic Heart Failire.
10 Bristow MR, Saxon LA, Boehmer J, Krueger S, Kass DA, De Marco T, Carson P, DiCarlo L, DeMets D, White BG, DeVries DW, Feldman AM;
Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure (COMPANION) Investigators. (2004). "Cardiac-resynchronization therapy
with or without an implantable defibrillator in advanced chronic heart failure". N Engl J Med 350 (21): 2140-50. PMID 15152059.
11 Cleland JG, Daubert JC, Erdmann E, Freemantle N, Gras D, Kappenberger L, Tavazzi L; Cardiac Resynchronization-Heart Failure (CARE-HF) Study
Investigators. (2005). "The effect of cardiac resynchronization on morbidity and mortality in heart failure". N Engl J Med 352 (15): 1539-49. PMID
15753115.
12 Bardy GH, Lee KL, Mark DB, Poole JE, Packer DL, Boineau R, Domanski M, Troutman C, Anderson J, Johnson G, McNulty SE, Clapp-Channing N,
Davidson-Ray LD, Fraulo ES, Fishbein DP, Luceri RM, Ip JH; Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT) Investigators. (2005).
"Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure." N Engl J Med 352 (3): 225-37. PMID 15659722.
13 Mandeep R. Mehra, Patricia A. Uber, Don B. Chomsky, Ron Oren ; Emergence of Electronic Home Monitoring in Chronic Heart Failure: Rationale,
Feasibility, and Early Results With the HomMed Sentry™-Observer™ System; Congestive Heart Failure, Volume?6,?Issue?3 , Pages137?-?139, 2000
by CHF, Inc
14 Small KM, Wagoner LE, Levin AM, Kardia SLR, Liggett SB. 2002. Synergistic polymorphisms of _1- and 2C-adrenergic receptors and the risk of
congestive heart failure. N Engl J Med 347(15):1135-1142
15 Craig Lehmann PhD CC (NRCC) FACB and Jean Marie Giacini BS; "Pilot Study: The Impact of Technology on Home Bound Congestive Heart
Failure Patients"; Home Health Care Technology Report, v1(4):50,59-60, 2004, Civic Research Institute.
16 Chronic Disease Management Toolkit; http://www.health.gov.bc.ca/cdm/practitioners/toolkit_facts.pdf
17 Fiona M Blyth, Ross Lazarus, David Ross, Michael Price, Gary Cheuk and Stephen R Leeder; "Burden and outcomes of hospitalisation for
congestive heart failure"; http://www.mja.com.au/public/issues/jul21/blyth/blyth.html ; article published on the Internet by The Medical Journal of
Australia
18 ScienceDaily. Retrieved October 17, 2008, from: http://www.sciencedaily.com/releases/1997/02/970218173035.htm
19 Vinson JM, Rich MW, Sperry JC, Shah AS, McNamara T. Early readmission of elderly patients with congestive heart failure. J Am Geriatric Soc.
1990;38:1290-1295.
20 Amsterdam EA, Cohn JN, Konstam MA, Pitt B. Today's workup for heart failure. Patient Care. 1995;29:58-73.
21 RydÈn-Bergsten, T., Andersson, F., The health care costs of heart failure in Sweden. Journal of Internal Medicine, Volume 246, Number 3,
September 1999 , pp. 275-284(10)
22 Leventhal MJ, Riegel B, Carlson B, De GS. Negotiating compliance in heart failure: remaining issues and questions. Eur J Cardiovasc Nurs.
2005;4:298?307.
23 Albert NM., Improving medication adherence in chronic cardiovascular disease, Crit Care Nurse. 2008 Oct;28(5):54-64
15
24 Sue Myers, Richard W. Grant, Nancy E. Lugn, Beth Holbert and Joseph C. Kvedar, Impact of Home-Based Monitoring on the Care of Patients with
Congestive Heart Failure, 2006; 18; 444 Home Health Care Management Practice
25 www.health.gov.bc.ca/cpa/publications/practicalguide.pdf
26 https://www.ncsbn.org/156.htm - accessed 20/10/08
27 http://www.hrsa.gov/telehealth/pubs/licensure.htm
28 http://www.tuaw.com/2008/09/29/first-look-heart-monitor/
29 http://nikeplus.nike.com/nikeplus/?locale=en_gb
16
10 Obesity
This placeholder was added online during our CAPSIL Meeting US. One of the CAPSIL partners has been tasked with creating this CAPSIL of
knowledge as an entry point to the CAPSIL wiki for interested parties.
17
11 Dementia
18
12 Depression
19
13 Osteoarthritis
Osteoarthritis is a disease that causes joint pain. The cartilage in the joints can become rough and thin. Beneath the cartilage the bone thickens and
grows outwards at the edges forming bony spurs. The synovium, the inner layer of the fibrous sleeve surrounding the joint that produces a lubricating
fluid, swells and may produce fluid that inflames the joint. Osteoarthritis affects us to 10 ? 20% of the elderly aged over 65, with approximately 8 million
in the UK.
Osteoarthritis can be caused by several factors, many of which are closely related to other age related issues discussed in CAPSIL. Age is the leading
cause and can begin to manifest itself in the late 40?s. Osteoarthritis tends to be more common in women than men and can be more severe.
Osteoarthritis can also be hereditary and run in families. The most common joints to be affected are the knees, hips, hands, spine, neck and big toe,
however, the disease can affect people very differently from causing little to no discomfort to being very painful with limited joint motion. Over time
symptoms tend to gradually increase, usually characterised by pain and stiffness of the joint. However, it is common to experience good spells and bad
spells where symptoms improve or worsen for periods of time. Another contributing factor to osteoarthritis is obesity as the increased weight puts more
pressure on the joints. Injury to the joint may also cause osteoarthritis later in life due to hard repetitive activities [1].
Due to the pain and stiffness in the joints caused by osteoarthritis, in severe cases, difficulty can experienced in many aspect of life such as pain,
stiffness in the joints, walking, and activities that require fine motor skills. This can cause a great impact on the quality of life by making simple tasks
difficult to conduct, such as activities involving fine motor skills including writing and sewing, and opening some food packaging or opening childproof
medicine bottles. Stiffness of the knees and hips, for example, may lead to a high likelihood of falling; stiffness may take some time to work out before
allowing free motion of the joint making initial movements awkward and painful.
13.1 Research
There are many research organisations and charities, such as the Arthritis Research Campaign and Arthritis Care, around the world working towards a
better understanding of osteoarthritis and developing better treatments for patients. Much of the work done by these organisations also helps provide
information for both families and suffers to aid their understanding and help them manage their illness.
Loughborough University has developed an osteoarthritis simulation suit [2] to enable doctors, nurses etc an insight into the effects of osteoarthritis. This
suit could also be used as a tool to enable homes, home appliances, food and product packaging, to name but a few, to be designed more
ergonomically for osteoarthritis sufferers [3]. Other age simulation suits have been developed that also include visual and hearing impairment, and
weights for the arms and legs [4].
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14 Parkinson's Disease
General Description
Parkinson's disease is a degenerative disease of the brain (central nervous system) that impairs motor skills, speech, and other functions. Parkinson's
disease belongs to a group of conditions called movement disorders. It is characterized by muscle rigidity, tremor, a slowing of physical movement
(bradykinesia) and, in extreme cases, a loss of physical movement (akinesia). The primary symptoms are the results of decreased stimulation of the
motor cortex by the basal ganglia, caused by the insufficient formation and action of dopamine, which is produced in the dopaminergic neurons of the
brain. Secondary symptoms may include high level cognitive dysfunction and subtle language problems. Parkinson's disease is both chronic and
progressive.
Issues
There?s no cure for Parkinson's disease. The costs associated with this disease in the US alone exceed $6 billion annually. This number is expected to
increase as the aging population continues to increase. Parkinson's disease is the most common disorder of movement, affecting at least 3% of the
population over the age of 65 years and more than 500,000 US residents. The characteristic motor features are development of rest tremor,
bradykinesia, rigidity, and impairment of postural balance. The primary biochemical abnormality in Parkinson's disease is deficiency of dopamine due to
degeneration of neurons in the substantia nigra pars compacta.
Current therapy of PD is based primarily on augmentation or replacement of dopamine, using the biosynthetic precursor levodopa or other drugs which
activate dopamine receptors [1]. These therapies are often successful for some time in alleviating the abnormal movements, but most patients
eventually develop motor complications as a result of these treatments [2, 3]. These complications include wearing off, the abrupt loss of efficacy at the
end of each dosing interval, and dyskinesias, involuntary and sometimes violent writhing movements. Wearing off and dyskinesias produce substantial
disability, and frequently prevent effective therapy of the disease [4, 5]. Although wearing off and dyskinesias often appear related to the timing of
medication doses, they are not simply a consequence of the pharmacokinetics of levodopa. Motor complications are virtually never observed early in the
treatment of PD; they appear only after prolonged treatment, usually several years. Furthermore, individuals who do not have PD but receive levodopa
for other indications do not develop motor complications. Experiments using controlled administration of dopaminergic drugs support these clinical
observations [6, 7]. From this work it is clear that motor complications are not simply a passive manifestation of pharmacokinetics, but rather are the
result of actively induced changes in brain function.
Justification
Currently available tools for monitoring and managing motor fluctuations are quite limited. In clinical practice, information about motor fluctuations is
usually obtained by asking the patient to recall the number of hours of ON and OFF time they have experienced in the recent past. This retrospective
approach is formalized in Subscale Four of the UPDRS (?Complications of Treatment?) which asks patients to report the duration of these symptoms in
terms of percent of awake time spent in each state. This kind of self-report is subject to both perceptual bias (patients often have difficulty distinguishing
dyskinesia from other symptoms) and recall bias. Another approach is the use of patient diaries, which does improve reliability by recording symptoms
as they occur, but does not capture many of the features that are useful in clinical decision making. In clinical trials of new therapies, both the
diary-based approach [8] as well as extended direct observations of the patients in a clinical care setting [9] have been used, but both capture only a
small portion of the patients daily experience and are burdensome for the subjects.
Research
Research is branching out to several areas including the use of neuroprotective agents, gene therapy, neural transplantation, and complementary
therapies. Neuroprotective treatments are at the forefront of the research on Parkinson?s disease, but are still under clinical scrutiny [10]. These agents
could protect neurons from cell death induced by the disease resulting in a slower progression of it. Agents currently under investigation as
neuroprotective agents include anti-apoptotic drugs, lazaroids, bioenergetics, antiglutamatergic agents and dopamine receptors [11]. Gene therapy and
neural transplantation are promising areas of research but years of research work appear to be necessary before clinical applicability can be claimed.
Nutrients have been used in clinical studies and are used by people with Parkinson?s disease to manage symptoms. None of these interventions is
effective in the long run and therefore the field has shown interest for the development of systems and methods to monitor individuals with Parkinson?s
disease with potential for facilitating the management of symptoms.
Major investments have been made in Parkinson's disease research by institutions such as the National Institute of Neurological Disorders and Stroke
and the Michael J Fox Foundation.
Future Vision"
Wearable monitoring systems could be used to identify the characteristics and severity of motor fluctuations in patients with Parkinson?s disease on the
basis of data recorded in the home and community settings. The accomplishment of this goal could greatly improve the quality of information available to
physicians treating patients with Parkinson?s disease. Such data would provide a quantitative, reliable and reproducible measure of the severity of
motor fluctuations that could be used as an endpoint measure in clinical trials of novel therapies thus aiding the development of therapies under
research.
References
[1] Standaert DG, Young AB, in Goodman and Gilman's Pharmacological Basis of Therapeutics, Tenth Edition, Hardman JG and Limbird LE, Editors,
McGraw-Hill, 549-620, 2001.
[2] Chase TN, ?Levodopa therapy: consequences of the nonphysiologic replacement of dopamine?, Neurology, 50(Suppl5): S17-S25, 1998.
21
[3] Obeso JA, Olanow CW, Nutt JG, ?Levodopa motor complications in Parkinson's disease?, Trends Neurosci, 23: 2-7, 2000.
[4] Lang AE, Lozano AM, ?Parkinson's disease. First of two parts?, N Engl J Med, 339(16): 1044-1053, 1998.
[5] Lang AE, Lozano AM, ?Parkinson's disease. Second of two parts?, N Engl J Med, 339(16): 1130-1143, 1998.
[6] Blanchet PJ, Papa SM, Metman LV, Mouradian MM, Chase TN, ?Modulation of levodopa-induced motor response complications by NMDA
antagonists in Parkinson's disease?, Neurosci Biobehav Rev, 21: 447-453, 1997.
[7] Mouradian MM, Heuser IJ, Baronti F, Chase TN, ?Modification of central dopaminergic mechanisms by continuous levodopa therapy for advanced
Parkinson's disease?, Ann Neurol, 27(1): 18-23, 1990.
[8] Parkinson Study Group, ?Evaluation of dyskinesias in a pilot, randomized, placebo-controlled trial of remacemide in advanced Parkinson disease?,
Arch Neurol, 58(10): 1660-1668, 2001.
[9] Adler CH, Singer C, O'Brien C, Hauser RA, Lew MF, Marek KL, Dorflinger E, Pedder S, Deptula D, Yoo K, ?Randomized, placebo-controlled study of
tolcapone in patients with fluctuating Parkinson disease treated with levodopa-carbidopa. Tolcapone Fluctuator Study Group III?, Arch Neurol, 55(8):
1089-1095, 1998.
[10] Bonuccelli U, Del Dotto P. "New pharmacologic horizons in the treatment of Parkinson disease". Neurology 67(2):30?38, 2006.
[11] Djaldetti R, Melamed E. "New drugs in the future treatment of Parkinson's disease". J. Neurol. 249(Suppl 2):II30?5, 2002
22
15 Stroke
General Description
Stroke is an insult of the brain caused by either an ischemic or a hemorrhagic event that affects the brain. A description of the pathology can be found for instance - on WebMD.
Stroke is a cause of long-term disability. According to the National Stroke Association, about 730,300 people suffer a stroke each year in the US.
Two-thirds of these individuals survive and require rehabilitation. Similar incidence of stroke is found in Europe and other industrialized nations.
Our CAPSIL on Stroke Rehab Management has more details on stroke rehabilitation.
Note: This CAPSIL entry point was added online during our CAPSIL Meeting EU.
23
16 Falls Prevention
Falls is one of the three geriatric giants and is a significant causes of injury in the elderly. Approximately 28-35% of people aged 65+ fall increasing to
32-42% for those over 70 years of age experience a significant falls event. The frequency of falls increases with age and frailty level. Falls have a
significant cost associated with the event. These costs can be assigned to two categories: Direct healthcare costs such as in-hospital treatments,
medication, utilization of services such as rehabilitation etc. Indirect cost through societal impact e.g. loss of economic productivity by family members
who must devout time caring for a faller. The WHO report on Falls reports [1] the average health system cost per single fall injury episode in the 65+ age
group was $1049. Among the different costs, hospital inpatient services where the most significant costing accounting for more than 50% of the total
overall costs.
The underlying risk factors in falls are varied and in many cases the these factors are inter-related.
http://s.wsj.net/media/fall_art_257_20080715174436.jpg
16.1 Falls Detection
The majority of fall-related injuries of the elderly are mainly caused by slipping due to environmental factors rather than tripping, but most wearable fall
detection devices are designed to capture dramatic falls. Falls detection devices fall into two broad categories, namely:
16.1.1 Body Worn Devices
• User-activated alarms and pendants
• Automatic wearable fall detectors
16.1.2 Non Contact Sensing
• Video monitoring-based fall detectors
• Floor Vibration-based fall detectors
16.2 Falls Prevention
Falls detections methods do little to eliminate the impact of a fall on an older person. At best falls detection systems reduce the response time to a falls
event. There is growing interest in Falls prevention through Gait Assessments. These assessment are based on clinical assessment models or
instrumental approaches which provide an emperical measure of Gait parameters.
16.2.1 Clinical Models
In clinic settings various models have been developed to determine a patient risk of falling based on a battery of standard clinic tests. These include
turning, bending, standing up from a chair, and walking. A wide range of clinical rating scales and functional test have now been evaluated in older
people to determine their ability to predict falls. These include sit-to-stand ability turning, bending down, tandem walk and Performance Oriented
Balance and Mobility Assessment (POMA).
• Performance Oriented Balance and Mobility Assessment (POMA)
• Berg Balance Scale (BBS)
• The Timed Up and Go Test
The benefit of these tests is that they require little or no expensive equipment and they are easy and quick to perform. However they can be subjective
in they way the tests are administered and the results interpreted. Instrument tests can provided a more non subjective and empirically based
approached to the assessment of gait and falls risks.
16.2.2 Gait Analysis Systems
Several studies have identified quantifiable gait markers that appear to distinguish between elderly "fallers" and non-fallers. These studies have relied on
data acquired from specialised Gait Analysis systems.
• GaitRite
• TRIL Gait Analysis Platform
• CODA
The diffulties with these systems is that migration from a clinical laboratory setting in to a home setting to provide on-going monitoring of gait is not
practical or cost effective.
24
16.2.3 Exercise
It has been reported in the literature that exercise has a major role to play in preventing falls. Also the type of exercise is important as some types are
likely to result in a greater reduction of falls risk.
16.3 Issues
16.3.1 Costs
Falls have a significant cost associated with the event. These costs can be assigned to two categories:
• Direct healthcare costs such as in-hospital treatments, medication, utilization of services such as rehabilitation etc.
• Indirect cost through societal impact e.g. loss of economic productivity by family members who must devout time caring for a faller.
The WHO report on Falls reports the average health system cost per single fall injury episode in the 65+ age group was $1049. Among the different
costs, hospital inpatient services where the most significant costing accounting for more than 50% of the total overall costs. The average cost of
hospitalization for falls related injury in the 65+ age group ranged from $6646 in Ireland to $17483 in the US. These costs are projected to increase to
$US 240 billion. A recent Health Care Executive report into Falls in Ireland indicated that these falls injuries in older people is costing the Irish over ?400
million per years [2]. They stated that if current trends continue it is estimated that costs will escalate to ?1billion by 2020. In addition to the direct costs,
falls incur indirect costs that impact family members such as loss of productivity. The average cost in lost earnings has been estimated to be
approximately ?40k
16.3.2 Sensing Issues
Current sensing technologies are reactive i.e. indicate when a fall has occurred. Technology needs to evolve to point where the sensor is collecting
information in a non contact fashion that can be used to determine a person risk of falling and trigger appropriate interventions before a fall event occurs.
16.3.3 Compliance
Blythe et al [3] have shown that compliance for pendant type devices is less than < 20%
16.4 Justification
(Scientific Basis/efficacy/evidence)
16.5 Research
TRIL Centre
16.6 Projects
TRIL Falls Prevention
APOLLO have developed a set of recommendations for preventing falls in older adults in the EU.
Players (links to VCs/Angels/Agencies/MNCs/SME) Procurement Business Models Standards Gaps Gaps in technology Gaps in the basic science Gaps
in operation Gaps in implementation Future Vision
16.7 References
1. http://www.who.int/ageing/publications/Falls_prevention7March.pdf
2. http://www.hse.ie/eng/Campaigns/right/Preventing_Falls_and_Fractures.shortcut.html?showDoc=1
3. M. A. Blythe, A. F. Monk and K. Doughty, Socially dependable design: The challenge of ageing populations for HCI Interacting with
Computers, Vol 17, Issue 6, December 2005, pp 672-689
4. http://www.fallsprevention.co.uk
25
17 Stroke Rehab Management
With Stroke, rehabilitation aims at restoring motor and cognitive functions [1-4]. After the acute treatment in the months following a stroke, there is a
progressive shift of the intervention from the inpatient setting to the outpatient setting and the home when a stroke home care program is available. The
implementation of interventions in the home environment requires technology to monitor patients and facilitate the administration of rehabilitation
protocols [5].
Issues
Effectively implementing interventions and improving outcomes in individuals post stroke requires relying on measures of the recovery of motor and
cognitive functions in response to interventions. The use of monitoring technology (e.g. wearable sensors) is attractive in this context because it opens
the possibility of assessing individual responses and consequently adjusting rehabilitation interventions on the basis of data gathered in the home and
community settings. Data collected in such context has the potential to reflect the impact of interventions on the real life of stroke survivors.
The administration of rehabilitation interventions can be facilitated by computer and robotic technologies. Their use in the home settings can potentially
extend duration and intensity of rehabilitation thus leading to larger gains in motor and cognitive functions. Technologies to facilitate the administration of
rehabilitation protocols in the home setting and methods to evaluate the effectiveness of such interventions are under development.
Justification
Given the need to limit the increase in healthcare costs, prolonged and intensive rehabilitation interventions cannot be delivered solely by relying on
services provided in inpatient or outpatient settings. Researchers are faced with the challenge of developing low-cost, home-based interventions that
have an impact similar if not superior to that achieved by using traditional therapeutic interventions and more recently introduced rehabilitation
approaches that require a significant amount of high-cost labor as it is the case for specialized clinical personnel.
Research
Monitoring technology (e.g. wearable, miniature sensors [6]) has been recently developed that could facilitate stroke rehabilitation. Researchers have
designed and implemented systems for tracking limb movements using inertial sensors. Others have focused on the development of e-textile solutions
to capture movement characteristics in individuals post stroke. Similarly, researchers have implemented and tested several robotic systems aimed at
facilitating stroke rehabilitation [7-11]. Several agencies support this work, including the European Commission, the National Institutes of Health [12], the
National Science Foundation, and the Japan Science and Technology Agency.
Commercial
Although several companies have developed wearable sensors, there are no commercially available systems to unobtrusively monitor motor gains in
subjects post stroke providing methods to assess motor gains associated with rehabilitation.
Noticeable companies in the field of wearable systems are RealTime and Smartex. RealTime licensed from Intel a wireless sensor platform called
SHIMMER (Sensing Health with Intelligence, Modularity, Mobility, and Experimental Reusability) that monitors real-time motion and physiological data.
This platform is marked by low-power consumption and large storage capacity. Smartex develops e-textile solutions to monitor individuals over extended
periods of time.
A few companies produce systems that rely on robotics for stroke rehabilitation. Among others, Hocoma, Interactive Motion Technologies, and Motorika
have developed systems that are now utilized in several clinical centers. Hocoma's flagship product is the Lokomat, a robotic gait orthosis that facilitates
treadmill gait retraining in patients with neurological conditions such as spinal cord injury, stroke, and multiple sclerosis. Hocoma has recently launched
a robotic system for upper extremity rehabilitation, the Armeo system [13]. The Armeo is an exoskeleton that provides weight support to facilitate the
performance of therapeutic exercises in individuals with hemiparesis. Interactive Motion Technologies is a spin-off of MIT's Newman Laboratory for
Biomechanics and Human Rehabilitation. This company manufactures the MANUS system [14-16], a robot for upper extremity rehabilitation. Motorika?s
product is the ReoTherapy system, a joystick-like robot that patients hold with their hand thus providing guidance during performance of therapeutic
exercises of the hemiparetic arm.
Currently available systems are not developed as yet for application in the home environment. These technologies are currently adopted by academic
clinical centers for experimentation, but are not part as yet of standard therapy programs. When technologies ready for home therapy will be introduced,
it is likely that associated costs will be covered via billing in the same way as currently done for traditional therapeutic interventions. It is conceivable that
these services will be initially provided by requiring out-of-pocket payment by patients and their families as it is unlikely at this point in time that specific
reimbursement codes will be soon available to cover these treatment modalities.
Standards
Although the development of standard protocols concerning the use of monitoring and robotic technologies to facilitate stroke rehabilitation is of
paramount importance, the field is still in its infancy and no standards have been developed so far. Future work is necessary to define procedures that
would allow one to obtain reliable assessment data concerning individual patient response to rehabilitation interventions in the home setting.
Rehabilitation interventions in the home setting that use robotics and computer systems should be standardized.
Gaps
26
Further advances are needed to make it possible to use monitoring, computer, and robotic technologies for stroke rehabilitation in the home setting.
These technologies are still too obtrusive and insufficiently user-friendly to envision their use in the medical home.
Although pilot studies have reported preliminary evidence of increased motor gains associated with the use of the above-mentioned technologies over
traditional therapy, larger clinical trials are needed to determine clinical criteria for its use. For instance, researchers have still not fully investigated the
complex relationship among therapy dosage, expected motor gains, and baseline impairment and functional limitation levels.
Future Vision
Current trends indicate that the duration of inpatient stay and the number of outpatient visits reimbursed by healthcare systems across the world are
decreasing. It is expected that growing emphasis will be put on developing home-based interventions to provide intensive therapy, which is known to be
associated with increased improvements in motor and cognitive outcomes. Monitoring systems, robotic and computer technologies to deliver
interventions will be developed soon to the extent necessary for adoption as complementary to interventions delivered in a clinical setting.
References
[1] J. Blennerhassett and W. Dite, "Additional task-related practice improves mobility and upper limb function early after stroke: a randomised controlled
trial," Aust J Physiother, vol. 50, pp. 219-24, 2004.
[2] J. H. Cauraugh, S. B. Kim, and J. J. Summers, "Chronic stroke longitudinal motor improvements: cumulative learning evidence found in the upper
extremity," Cerebrovasc Dis, vol. 25, pp. 115-21, 2008.
[3] N. E. Mayo, S. Wood-Dauphinee, S. Ahmed, C. Gordon, J. Higgins, S. McEwen, and N. Salbach, "Disablement following stroke," Disabil Rehabil, vol.
21, pp. 258-68, 1999.
[4] S. Young and K. H. Kong, "Emerging therapies in stroke rehabilitation," Ann Acad Med Singapore, vol. 36, pp. 58-61, 2007.
[5] C. R. Carignan and H. I. Krebs, "Telerehabilitation robotics: bright lights, big future?," J Rehabil Res Dev, vol. 43, pp. 695-710, 2006.
[6] P. Bonato, "Advances in wearable technology and applications in physical medicine and rehabilitation," J Neuroeng Rehabil, vol. 2, pp. 2, 2005.
[7] J. Hidler, D. Nichols, M. Pelliccio, and K. Brady, "Advances in the understanding and treatment of stroke impairment using robotic devices," Top
Stroke Rehabil, vol. 12, pp. 22-35, 2005.
[8] B. Husemann, F. Muller, C. Krewer, S. Heller, and E. Koenig, "Effects of locomotion training with assistance of a robot-driven gait orthosis in
hemiparetic patients after stroke: a randomized controlled pilot study," Stroke, vol. 38, pp. 349-54, 2007.
[9] R. J. Jaeger, "Rehabilitation robotics research at the National Institute on Disability and Rehabilitation Research," J Rehabil Res Dev, vol. 43, pp.
xvii-xx, 2006.
[10] H. I. Krebs, N. Hogan, B. T. Volpe, M. L. Aisen, L. Edelstein, and C. Diels, "Overview of clinical trials with MIT-MANUS: a robot-aided
neuro-rehabilitation facility," Technol Health Care, vol. 7, pp. 419-23, 1999.
[11] H. Schmidt, C. Werner, R. Bernhardt, S. Hesse, and J. Kruger, "Gait rehabilitation machines based on programmable footplates," J
Neuroengineering Rehabil, vol. 4, pp. 2, 2007.
[12] M. Weinrich, "National Institutes of Health support of rehabilitation robotics research," J Rehabil Res Dev, vol. 43, pp. xxi-xxii, 2006.
[13] R. J. Sanchez, J. Liu, S. Rao, P. Shah, R. Smith, T. Rahman, S. C. Cramer, J. E. Bobrow, and D. J. Reinkensmeyer, "Automating arm movement
training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment," IEEE Trans Neural Syst Rehabil Eng,
vol. 14, pp. 378-89, 2006.
[14] M. L. Aisen, H. I. Krebs, N. Hogan, F. McDowell, and B. T. Volpe, "The effect of robot-assisted therapy and rehabilitative training on motor recovery
following stroke," Arch Neurol, vol. 54, pp. 443-6, 1997.
[15] S. E. Fasoli, H. I. Krebs, and N. Hogan, "Robotic technology and stroke rehabilitation: translating research into practice," Top Stroke Rehabil, vol.
11, pp. 11-9, 2004.
[16] H. I. Krebs, M. Ferraro, S. P. Buerger, M. J. Newbery, A. Makiyama, M. Sandmann, D. Lynch, B. T. Volpe, and N. Hogan, "Rehabilitation robotics:
pilot trial of a spatial extension for MIT-Manus," J Neuroeng Rehabil, vol. 1, pp. 5, 2004.
27
18 Weight Management
General Description
Obesity is the presence of excess amount of body fat and a person is considered to be obese if his/her weight is 20% or more above the normal weight.
The Body Mass Index or the BMI is the measure used to determine overweight and obesity. If The BMI is in between 25-29.9, the person is said to be
overweight whereas if the BMI is more than 30, the person is considered to be obese. In America, 127 million adults are overweight and 60 million
obese with obesity being cited as the second leading cause of preventable death. (Obesity in America.org). The elderly population also shows an
alarming trend of increase in this condition with 32.1% of the male population and 37.9% of the female population aged 65-74 yrs considered obese in
2001-2004 according to the CDC.(http://www.cdc.gov/nchs/data/hus/hus07.pdf#074) Public awareness of the obesity epidemic and its role as a
predisposing factor for several chronic diseases has led to an increasing interest in losing weight. Almost 30-40% of the American population is currently
using diet to lose weight.(Phillips, 2008). There is growing evidence to suggest that e health behavioral interventions especially tailored interventions
have the potential to lead to long lasting weight loss and weight maintenance behavioral changes.(Tufano, 2005).This is essentially true in the case of
the elderly population where successfull weight management and weight maintenance may be an important aspect of reducing the incidence of chronic
diseases thereby prolonging independent high quality community living.
Issues
? Attrition: Research shows that online health programs including those that deal with weight management are notorious for their rates of attrition;
reasons being lack of motivation, technical problems unrelated to the intervention e.t.c.
? Awareness and computer literacy: People in the geriatric age group are definitely interested in learning and using online programs related to health to
improve their quality of life. However, more studies are required to gauge the extent of awareness and interest among vulnerable sections in this age
group viz. people living in remote areas, ethnic minority groups etc.
? System issues: Installation and maintenance issues related to the system has to be looked into as these are important reasons for discontinuation/lack
of interest in adopting e weight management programs.
? Usability issues: Age related specific issues such as small font size, lot of information in a single web page, lack of proper instructions for use need to
be solved (Nahm, 2004).
? Privacy and security: These are emerging as major concerns not only for the end user population but for all the stakeholders involved.
? Affordability: System affordability as well as membership fees required for access to the various sites needs to be tackled so as to arrive at a
satisfactory solution to all those concerned namely,the end user, manufacturers/vendors as well as the third party payors.
Justification
Obesity is a risk factor for hypertension, atherosclerotic heart disease, type 2 diabetes and several other chronic illnesses like cancer, COPD,
musculoskeletal disorders, sleep apnea, gall bladder disease and many more.(Phillips, 2008; Franssen, 2008, Finkelstein, 2005). Visceral fat
accumulation is associated with insulin resistance and hypertension. Thus Obesity is a major cause of mortality and morbidity in the world increasing
theexpectations from an already overburdened health care system.. (Phillips, 2008).
Obese (BMI- 30 to 34.9) and severely obese (BMI > 35) individuals have 14% and 25% more physician visits as compared to normal weight individuals
and 34% and 74% more in patient days as compared to normal weight individuals. (Finkelstein, 2005). The average annual medical costs associated
with obesity in the United states are between 5-7% of annual health expenditures. (Finkelstein, 2005). Moreover, since obese individuals are covered by
both medicare and Medicaid, the government finances half the annual medical costs due to obesity. The taxpayer bears around $175 /yr which goes
toward obesity related expenditures. (Thompson, 1998).
Thus it is exceedingly clear that obesity related problems cost billions of dollars to the government, industries and the tax payers. This does call for a
urgent and ubiquitous requirement for efficient, effective and proactive tools for health behavior change.Thus the e health weight management program
has the potential to effectively maintain health, improve quality of life and reach millions of people at a relatively low cost. (Rothert, 2006).
Research
There is evidence to support the fact that well designed weight loss programs delivered through the internet produces significant weight loss. (Glasgow,
2007). Moreover, Kaiser permanente's large scale randomized controlled trial showed that web based tailored weight management program has the
potential to effect significantly greater weight loss as compared to an infomration only weight management programs. Participants of this trial also
reported that the tailored program was personally relevant, helpful and easy to understand. (Rothert, 2006). However, the efficacy of such online
programs in long term maintenance of weight still remains ambiguous.(Weinstein, 2006). A Phase 11 clinical trial sponsored by Robert wood johnson
foundation began in 2004 with the aim of evaluating the efficacy of MiDieta e health portal for weight loss among the Hispanic community with the
results expected shortly. (http://clinicaltrials.gov/ct2/show/NCT00372606).
Commercial Products
Weight watchers online
http://www.weightwatchers.com/index.aspx
This website allows instant access to state-of-the-art and easy-to-use interactive weight-loss tools and advice. Some of the features of this program are
the plan information is accessible at all times, availability of 1500 recipes and meal ideas, 60 workout demonstrations, easy access to food and exercise
habits with new interactive tools, continuous weight monitoring for progress, provision of personalized goals on weight maintenance, access to resource
library of articles and expert advice and message board for support with high privacy standards.
28
E-Health fitness
http://www.e-health-fitness.com/
This website provides easy access to plain language health and fitness information, support and resources and has the following features; health and
fitness information on physical and mental illnesses, addiction, cancer, weight, elder care and nutrition, aerobics, anaerobics, walking, lifting, stress and
weight loss exercise; Articles/Books reviews and book lists realted to health and fitness;Forums/Blog where people can talk to the site coordinators or
each other for support and encouragement and a health Shop.
Revolution Health
http://www.e-health-fitness.com/
This website is dedicated to healthtopics and healthy living, one such topic being weight management. Some of the features include weight
management guide including weight and diet options, motivation, exercise, challenges and maintenance; weight management topics including nutrition,
metabolism, diets, weight maintenance; Forums, blogs for discussions and social support. Moreover, Revolution health and Spark people in partnership
have the following weight management tools; ? Sparkdiet- four stages that combine healthy tips and tools with motivation and confidence to improve
lifestyle ? calorie counter- customized tools for determining calorie intake ? exercise demos- easy demos targeting major muscles ? workout generatorpersonalized workout
eDiets
http://ediets.com/
ediets is a leading provider of online weight loss services, online diets and customized fitness plans and weight loss information and products based in
Fort Lauderdale, Florida.It provides access to nutrition and fitness expert; community groups for social support and encouragement including success
stories, support boards and support from certified experts; custom fitness plans; list of healthy recipes and e tools to track progress.
My Diet
http://christus.mydiet.md/support/default.asp
This is a personalized weight control program with the following features ? Daily, customized menus ? Personalized exercise program ? A weekly
grocery shopping list ? NutriNews- newsletter on diet, fitness, and healthy living ? Health news and daily tips ? Online recipe library ? Support from the
MyDiet Community
eHealth companion
http://www.ehealthcompanion.com/
This product website provides proactive healthcare. Their wellness, Prevention & Fitness programs offer guidance, practical resources and
encouragement to help people selecting healthier lifestyle. Resources include health assessments, effective weight-loss strategies, extensive exercise
library and personalized tele-coaching by trained experts.
Traineo
http://www.traineo.com/
Weight management site that helps track workouts as well as sends weekly progress reports via email to 4 motivators of the clients choice; easy-to-use
software allows client to choose what they'd like traineo to report to the motivators so they can provide the encouragement, social support and
accountability needed to achieve specific goals. Traineo tools: ? Determination of calories burnt at the gym/workouts ? Training diet rating ?
Visualization of progress (graphs) ? E mails with traineo motivators ? Meet others in traineo community for social support and encouragement ?
Personalized traineo.com page
e weight loss center
http://www.eweightlosscenter.com/
e Weight Loss Center features nutritional information, interactive tools, support groups to help members.The website offers interactive tools that helps
members customize a program to achieve their specific goals as well as providing a variety of information on nutrition, fitness and health at the click of a
mouse. Tools: ? 24 hr support thru private chat room, ? message boards, ? buddy system, ? regular online meetings, ? motivational emails from
personal weight loss coach
Procurement
Business Models
Standards
29
Gaps
Gaps in technology
Systems need to be more user friendly (especially geared for easy use by elderly and first time users)
Gaps in the basic science
? dearth of research of applicability and feasibility of online programs in the elderly, various ethnic groups and socioeconomically disadvantaged.
? dearth of studies focusing on efficacy of online weight management programs for long term weight maintenance.
? dearth of studies on cost effectiveness and feasibility of inclusion of such programs in insurance plans.
Gaps in implementation
Feasibility studies for large scale implementation in the general population.
Future_Vision
Online, home/work based , low cost, tailored, effective, long term, weight loss management programs at affordable prices,appropriate for various
subpopulations with regards to age, ethnicity and socio econoomic status so as to improve lifestyle and motivate behavior change leading to a healthier
population enjoying good quality of life.
References
1)Finkelstein, E., Ruhm, C., Kosa, K., (2005). Economic causes and consequences of obesity. Annu Rev public Health, 26, 239-57.
2)Franssen, F., O?Donnell, D, Goossens, G., Blaak, E., Schols, A., (2008). Obesity and Lung. 5 Obesity and COPD, Thorax, 63 (12), 1110-1117.
3) Glasgow, R., et al (2007). Reach, engagement, and retention in an Internet-based weight loss program in a multi-site randomized controlled trial. J
Med Internet Res. 2007 May 9;9(2):e11.
4)Nahm,E., Preece, J., Resnick, B., Mills, M., (2004). Usability of health websites for older adults: a preliminary study.Comput Inform Nurs, 22 (6),
326-34.
5)Phillips, S. (2008). Obesity, Weight Loss, and cardiovascular Health: Is Oxidative Capacity a Missing Link? American Journal of Hypertension, 21 (12),
1277.
6) Rothert, K., et al (2006). Web-based weight management programs in an integrated health care setting: a randomized, controlled trial. Obesity (Silver
Spring). 2006 Feb;14(2):266-72
7)Strum, R., (2002). The effects of obesity, smoking and drinking on medical problems and costs. Health Aff, 21, 245-53.
8)The endocrine society and the hormone foundation, Obesity in America. Retreived from the net on 21st Nov 2008 from
http://www.obesityinamerica.org/bythenumbers.html
9)Thompson D, Edelsberg J, Kinsey KL, Oster G. 1998. Estimated economic costs of obesity to U.S. business. Am. J. Health Promot. 13(2):120?27
10)Tufano, J. (2005). Mobile eHealth Interventions for Obesity: A Timely Opportunity to Leverage Convergence Trends
11)WebMD, Obesity- retrieved from the net on 21st Nov 2008 from http://www.webmd.com/diet/what-is-obesity
12) Weinstein, P., (2006). A review of weight loss programs delivered via the Internet. J Cardiovasc Nurs. 2006 Jul-Aug;21(4):251-8; quiz 259-60
13)http://www.e-health-fitness.com/
14)http://www.revolutionhealth.com/community-blogs/weight-management
15)http://www.ediets.com/
16)http://clinicaltrials.gov/ct2/show/NCT00372606
17)http://christus.mydiet.md/support/default.asp
18)http://www.ehealthcompanion.com/products.aspx
19)http://organizedwisdom.com/helpbar/index.html?return=http://organizedwisdom.com/Weight_Loss&url=traineo.com/
30
19 Cognitive Training
19.1 General Description
The goals of cognitive training are to enhance a person's capacity to process and interpret information and to improve the person's ability to function in
all aspects of family and community life. The focus can be on either on delaying cognitive decline due to aging or neurological disease or on remediation
of current cognitive deficits. The cognitive dimensions typically addressed include memory, concentration and attention, perception, learning, planning,
sequencing, and judgment. In addition, restorative training focuses on improving a specific cognitive function, whereas compensatory training focuses
on adapting to the presence of a cognitive deficit. Compensatory approaches may have restorative effects at certain times. Some cognitive rehabilitation
programs rely on a single strategy (such as computer-assisted cognitive training), while others use an integrated or interdisciplinary approach. A single
strategy program can target either an isolated cognitive function or multiple functions concurrently.
Technology for cognitive training has blossomed in recent years. A report from SharpBrains (http://www.sharpbrains.com/) estimates that the market for
brain fitness software reached $224 million in the US in 2007. The interventions are typically designed for the consumer market, although many are
licensed to senior facilities as well.
The evidence for the effectiveness of these systems is beginning to emerge but quite limited. Many of the commercially available systems have not
undergone rigorous testing. However, there seems to be an understanding on the part of consumers that it is important to "use it or lose it." One of the
difficulties in cognitive training is that repetitive practice on single skills usually does not transfer to other cognitive domains or to general functionality in
daily life. It is important that interventions create novel situations and problems to solve.
19.1.1 Issues
(problems/challenges) being addressed in this are - some cross cutting
New field Many systems lack scientific evidence Do interventions transfer to functional independence? Do interventions delay the onset of Alzheimer's
and other dementias? Do interventions help remediate cognitive problems with stroke, MS, etc.? Do interventions help elder people to access to new
technologies? (simplify the training to their usage)
19.1.2 Justification
Cognitive performance is a key health concern of elders throughout the world. In fact, maintaining cognitive health is often the most important factor in
being able to age in place. Nearly 50% of all people over the age of 85 are found to have a measurable decline in cognitive function (Callahan, 1995).
However, common clinical practice does not offer methods for detecting cognitive decline at an early stage, when therapies may be more effective.
Recent research has demonstrated the importance of detecting cognitive decline in an early stage (Chen, 2000). Some cognitive issues have
immediately treatable causes, such cognitive disturbances due to medication interactions or short-term medical conditions. However, even with
long-term conditions, such as dementia, there are many new therapies that researchers presume would have improved efficacy with earlier detection.
19.1.3 Research
Recent research has shown evidence of neural plasticity at older ages and that cognitive exercise or training may help delay age-related cognitive
decline and various forms of dementia.Meta-analyses and reviews of studies on the effects of cognitive training with older adults have concluded that
training on a specific cognitive task improves subsequent performance on that task. More recent studies have addressed the additional issues having to
do with the durability of effects over time, the transfer of effects to other cognitive domains, and whether effects generalize to everyday activities for
older adults. For example, in the early stages of the ACTIVE study which looked at memory training, reasoning training, and processing speed training,
in the 2 year follow up no evidence of transfer to other domains was found. However, the more recent 5-year follow up of the ACTIVE study examined
instrumental activities of daily living (IADLs) and found that reasoning training specifically protected against IADL decline over the 5 years. Several other
interventions have shown evidence for the transfer of cognitive gain or extended effect. These have ranged from focused interventions such as
self-generated strategy training or reasoning training to combined interventions such as a mix of cognitive and motor exercises; memory / problem
solving / speed-of-processing; image encoding / attentional tasks / relaxation exercises; and computer-based training on multiple tasks. However, the
evidence has not been consistent for older adults, and transfer and duration remain a challenge for this population.
Players Projects Funding
19.1.4 Commercial Products
19.1.4.1 Luminosity
http://www.lumosity.com/ Cognitive training with computer games Recommended 10 minutes/day Feedback and adaptive models Sharp Brains
http://www.sharpbrains.com/ Newsletters Expert chats online Bboards on various cognitive health topics Recommended cognitive games Posit Science
http://www.positscience.com/ Computer exercises designed to improve the user's ability to incorporate sensory information Basic Classic - auditory
exercises to improve attention Insight - Computer games for visual processing MyBrainTrainer 39 short (one to three minutes each) interactive exercises
requiring the user to respond rapidly to a series of random stimuli. Each exercise isolates a specific region of the brain. 21-Day Training - structured
31
progression of exercises, 10-20 minutes / day MegaStats - displays average scores by age, occupation and gender BrainBoard - the place for members
to trade tips and thoughts on brain training Newsletter - either online or in email box BrainDiary - enables one to monitor performance across several
variables, e.g., time of day, hours of sleep, consumption of caffeine Happy Neuron Brain Fitness CD-ROM Recommend 20 minutes/day Addresses
memory, language, logic, concentration, visual/spatial skills CogniFit http://www.mindfit.com/ MindFit - computer exercises adapted to the individual;
sharpening memory, eye-hand coordination, multi-tasking abilities Recommended 20 minutes per day, 3x/wk MindFit Back on Track - for women
recovering from Br Ca Golden DriveFit - sharpening driving skills at advanced age
19.1.4.1.1 Nintendo
Brain Age - handheld game device with cognitive computer games Calculates "brain age" Least scientific; least tested Spry Learning
http://www.sprylearning.com/ 9 cognitive computer games designed for elders Embedded monitoring algorithms Adaptive presentation Being integrated
with a cognitive health coaching tool at OHSU These are all commercially available cognitive training systems, with varying degrees of scientific
underpinnings and evaluations. All offer a potentially inexpensive and scalable approach to improving cognitive health. The clinical effect size of such
interventions under maintenance use has not been fully tested. In addition, long-term usage without motivational encouragement could be a severe
barrier. New work in cognitive health coaching, designed to motivate and manage cognitive health remotely is under development. However, this is a
nascent field that is changing rapidly. A scalable and low-cost approach could be very effective in keeping elderly people independent and able to age in
place.
Players (links to VCs/Angels/Agencies/MNCs/SME)
19.1.4.2 Commercial vendors
Posit Science
Sharp Brains
Lumosity
19.1.5 Procurement
19.1.7 Standards
19.1.8 Gaps
Gaps in technology Gaps in the basic science Gaps in operation Gaps in implementation
19.1.9 Future_Vision
19.1.10 References
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35
20 Social Connectedness
Co-located Social Connectedness
Distributed Social Connectedness
Generally speaking one's social circle, or more specifically one's social network, includes ones friends, family, colleagues and acquaintances. Social
connectedness is a psychological term which describes the duration, frequency, familiarity and reciprocal nature of the relationships people have with
others in this circle or network.
Social Connectedness is widely delieved to play an important role in a person overall health and wellbeing. Many Gerontology studies have found that a
strong Social Network (in terms of the strength of relationships an individual has with other individuals in his/her community) plays an important role in
helping prevent and slow down the onset of cognitive and physical disorders associated with aging [4].
Social Network Services offer interactive web based environments for enabling people to connect and maintain social connectons, thus supporting the
mirroring of real communities into virtual ones. Initially, online social networks were the playgrounds of young people, from early teens to mid-twenties
but changed rapidly. With the ongoing niche stratification of the social network space there are many social network services targeting specific
audiences, e.g. Eons for those aged 50+, LinkedIn for maintaining and creating professional ties. It is therefore possible to envision a spread of social
network services targeted at ageing people, who are more likely to suffer from emotional isolation.
Social networks services is therefore a phenomena which is moving faster from its initial realtively small market segment to potentially involving users
from the early education age until the rend of life.
In recent years social networks have exploded in popularity and diversity, with rough estimates indicating online social networks are a regular part of
hundredths of millions of people's online lives.
36
20.1 Online Social Networks
Initially online social networks were the playgrounds of young people [14], from early teens to mid-twenties. To an extent online social networks are still
associated with a younger audience but that is rapidly changing. With the ongoing niche stratification of the social network space there are many social
networks targeting specific audiences, e.g. Eons [32] for those aged 50+, LinkedIn [31] for maintaining and creating professional ties.
Online social networks enable people to:
• Create online personal profiles
• Form connections with friends with have also created personal profiles, i.e. have a large circle of friends on a social network
• Easily update their profiles to share what is happening in their lives with their friends, i.e. profile updates and blog updates
• Share content, such as photographs and videos
• Engage in shared conversations between multiple groups of friends, e.g. share comments about shared photographs
• Communicate privately
• Create and engage in online interest groups
• Play games together and playfully interact, e.g. throw a (virtual) Frisbee to a friend
In "Social Network Sites: Definition, History, and Scholarship" Boyd and Ellision [13] defined social network sites (SNS) as web-based services that
allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a
connection, and (3) view and traverse their list of connections and those made by others within the system. The nature and nomenclature of these
connections may vary from site to site.
What differentiates SNS from previous social technologies, such as newsgroups and bulletin boards, is that SNS help members make their social ties
visible.
20.1.1 Offline Social Networks
Over the decades many gerontology studies have found that strong social networks play an important role in helping prevent and slow down the onset of
cognitive and physical disorders associated with aging [7][8].
When designing gerontechnologies there is a danger of assuming overly simplistic models of what it means to be old, and what the needs of older
people are [11]. Therefore a deep understanding of why offline social networks are beneficial, and what are the important properties of them is required
when designing online social networks for elders.
20.1.1.1 Online & Offline Social Network Fusion
Social network sites are commonly used to reinforce existing relationships, rather than building new friendships. Though sometimes interest groups can
organize meet ups where members meet in the cafe or bar for offline socialisation.
When considering SNS in terms of elders there are opportunities to investigate and build SNS that strongly intersect with the physical world. Such as
enabling an elder to purse independent living while enabling their extended network to act as a loosely bound social group remotely interacting with and
monitoring the elder. The elder's status could be automatically updated via passive health monitoring technologies, etc.
20.1.1.2 Elder Social Networks
Noteworthy elder social networks that are mentioned in this document include Eons and Boomj, which are US based elder networks, while Platinnetz is
based in Germany and is apparently going strong. Then there's Saga Zone, which came out of the UK Saga Publishing Group that has been published
magazines aimed at the elder market for many years.
20.1.1.3 Staying Abreast Of SNS Research & Statistics
Social networks are rapidly evolving with new competitors, initiatives and opportunities regularly emerging, therefore [25] and [24] are useful for staying
up-to-date with fluctuating statistics about SNS popularity, membership growth and web visitor traffic.
For a regularly updated list of recent publications concerning social networks see the online bibliography maintained by danah boyd at [15].
20.1.1.4 Social Network Growth: Worldwide
A comScore survey [21] During the past year, the total North American audience of social networkers has grown 9 percent compared to a much larger
25 percent growth for the world at large. The Middle East-Africa region (up 66 percent), Europe (up 35 percent), and Latin America (up 33 percent) have
each grown at well-above average rates.
37
Social Networking Growth by Worldwide Region [21]
June 2008 vs. June 2007
Total Worldwide Audience, Age 15+ - Home and World Locations
Source: comScore World Metrix
Unique Visitors (000)
Jun-07
Jun-08
Percent Change
Worldwide
464,437
580,510
25%
Asia Pacific
162,738
200,555
23%
Europe
122,527
165,256
35%
North America
120,848
131,255
9%
Latin America
40,098
53,248
33%
Middle East - Africa
18,226
30,197
66%
20.1.1.5 Social Network Penetration: Europe
Another comScore survey released in October 2007 indicated that Europeans are rapidly embracing social networks, a quote from [20] tells us that The
European social networking community stood at 127.3 million unique visitors in August reaching 56 percent of the European online population. U.K.
participation in social networking usage proved to be the highest in Europe, with 24.9 million unique visitors 78 percent of the total U.K. online population
now belonging to the country's social networking community.
European Usage of Social Networking Sites [20] Selected Countries
Ranked by Total Unique Visitors Age 15+
August 2007
Total Unique Visitors
(000)
% Reach of Country's Total Online
Population
Average Hours per
User
Average Pages per
User
Average Vists per
User
Europe
127,297
56.4
3.0
523
15.8
U.K.
24,857
77.9
5.8
839
23.3
Germany 15,475
46.9
3.1
423
13.8
France
13,332
49.6
2.0
476
16.8
Spain
8,828
61.5
1.8
251
14.9
Italy
8,736
49.3
1.8
346
12.6
* Age 15+, home & work locations; Excludes traffic from public computers such as Internet cafes or access from mobile phones or PDAs
20.1.1.6 Social Network Visitation To Selected Social Networks: Worldwide
Visitation to Selected Social Networking Sites by Worldwide Region [22]
Total Worldwide Home/Work Locations Among Internet Users Age 15+
June 2007
North America
Latin America
Europe
Middle East-Africa
Asia Pacific
MySpace
62.1%
3.8%
24.7%
1.3%
8.1%
Facebook
68.4%
2.0%
16.8%
5.7%
7.1%
Hi5
15.3%
24.1%
31.0%
8.7%
20.8%
Friendster
7.7%
0.4%
2.5%
0.8%
88.7%
Orkut
2.9%
48.9%
4.6%
0.6%
43.0%
Bebo
21.8%
0.5%
62.5%
1.3%
13.9%
38
Tagged
22.7%
14.6%
23.4%
10.0%
29.2%
20.1.1.7 Popularity Of Different Social Networks Worldwide
Website visitor traffic was used to measure SNS popularity. Map from [24] for November, 2008. Visitor traffic measured via Alexa. Other useful
visualizations of social networks could be explored with techniques mentioned in [18].
20.2 Use Case
The proposed user of a online social network is an elder, who wishes to connect with new people who share common interests, and continue connecting
with people in their established social network.
Below's Use Case's are usefully considered in the context of European Ageing Well Action Plan, especially concerning the following e-Inclusion goals
[17]:
• Ageing well at work or active ageing at work: staying active and productive for longer, with better quality of work and work-life balance with
the help of easy-to-access ICT, innovative practices for adaptable, flexible workplaces, ICT skills and competencies and IT enhanced learning
(resp. e-skills and e-learning).
• Ageing well in the community: staying socially active and creative, through ICT solutions for social networking, as well as access to public
and commercial services, thus improving quality of life and reducing social isolation.
• Ageing well at home: enjoying a healthier and higher quality of daily life for longer, assisted by technology, while maintaining a high degree
of independence, autonomy and dignity.
39
20.2.1 Friending
Friending is a core property of SNS. The act of friending makes explicit the social connectedness between individuals. Friend's share profile information
and access to each other's virtual private online spaces.
Friending creates a two-way connection between individuals. The level of granularity of that connection is currently very simple, i.e. a person is either a
complete friend with access to all your profile information and updates or they are a stranger, though there is increasing granularity around Friends of
Friends.
An interesting possibility with SNS for elders is to give elders greater control over what information they share with their social network. Ambient health
monitoring devices could become part of an elder's profile, then the elder can choose to let different people and groups in their social networks become
'friends' with the ambient health monitoring devices. In this way health monitoring devices, and related tools, become an extension of the elder's
personal identity that they are empowered to have control over. Other possibilities include enabling the elder to share nursing and staff activities in
assisted living facilities [1], where the nursing and staff activities concern the elder.
By leveraging an elder's social network elder's can be given the choice of choosing whom to rely upon. Further possibilities then arise, such as enabling
elder's to form friend networks around collaborative co-monitoring and peer-to-peer assistance [4][2][3].
20.2.1.1 Profiles (Virtual Private Spaces / Online Avatars)
Profiles are each SNS member's virtual avatar. They represent the user online.
Profile updates are automatically pushed out to friends, e.g. friend A has uploaded a new photo or changing set their status to say Going into town for a
coffee in X cafe at 11am. When person logs into their social network they can easily see their friend's most recent updates and actions.
Many SNS support extending profiles via custom written applications and support different layouts and visual styles. These profile extensions often
enhance the core functionality of profiles but can introduce inconsistencies in the interface and user experience.
For those unfamiliar with ICT attention will need to be paid to interface consistency. There is a trade off between the ability for people to customize their
profiles, making them their own, and creating something so unfamiliar as to be confusing.
Profile adaptability touches upon a number of core concerns for elder digital inclusion and aging well. These concerns are raised in the Ethics of
e-Inclusion of older people [17] - on p12 it is written In Europe, the digital divide is basically age-related. According to the European Commission's 2005
Benchmarking Report, 38 per cent of EU citizens were regular users of the Internet, but only 8 percent of people over 65 were regular users. The digital
divide cannot be characterised solely as a consequence of socio-economic variables nor can it be conceptualised solely in terms of socio-economic
priorities. Social dynamics, personal motivations and cultural elements are as important as economic factors. Digital inclusion, in practice, implies
changes affecting all these threads of the social fabric and promises benefits to society including economic development, health care improvements and
enhanced levels of social inclusion.
With adaptability the digital divide can be made worse, or it can be used to improve inclusivity, e.g. enable graceful profile adaption to each elder's
cognitive, physical, social and cultural background and abilities.
Consumption of profile updates can be enhanced for elders. For example with Ben Arent's Jive elder's can see their friend's profile updates via a
simplified tactile interface [27].
Recent studies of teen users of social networks are finding many younger users are implicitly learning and developing technically sophisticated skill sets
through 'playing' with their profiles and social network, e.g. templating their blogs. Can the same process be duplicated with elders? For example
enabling profile adaption by enabling elders to create profile extensions themselves. Currently extending profile functionality in SNS requires writing the
extensions using standard computer programming languages, which required a high degree of specialist knowledge. Could a visual or tactile
programming language be designed for elders, which enables non-technical elders to rapidly create profile extensions that they can share with their
peers?
20.2.1.2 Photo, Video & Media Sharing
Photo and media sharing is an important aspect of sharing between friends on many social networks. Photo sharing on social networks makes it easy
for friends to collaboratively create shared photo albums of the same or different events, then people who have access to the photos can leave
comments for others to read and respond to.
Transferring the photos from a digital camera, then to a computer and then uploading them online can be a challenging technical and user experience.
Though a number of applications for Apple's iPhone are available that simplify the whole process; simply take a photo and its automatically uploaded
online [30].
20.2.1.3 Social Gaming
Social networks also feature many casual games, such as Scrabble, Poker and Bridge. Playing against friends and with friends is easy because friends
are easily contactable via the game interfaces that are extensions of the SNS interfaces.
40
A number of opportunities arise, including encouraging gaming for maintaining cognitive function and preventing physical decline. More interesting
possibilities arise with the opportunity to investigate integrating SNS's with alternative reality gaming (ARG), i.e. use the physical world as platform for
games [22]. Could SNS and ARG games be created that implicitly encourage community development, elder interaction and socialization?
Alternatively rather than requiring external groups to develop the games could elders themselves be empowered to create the games, thereby
encouraging active aging and acquisition of ICT skills? <[7][8]
20.2.1.4 Integration With Other Elder Services (Everything Network)
Numerous elder services could be integrated into social networks, e.g. household billing, security, independent living, pensions, etc. By integrating them
into social networks an elder would have a single point of coordination for many different resources. For example an elder could receive information on a
pension update, then they elder could choose to share the update in their profile while seeking advice from their friend network.
Including services and devices into elders social networks would build on the strengths of social networks, i.e. making explicit social connectedness [9].
Rather than only making explicit friendship, an elder social network could make explicit many of the relationship they have with the world. By making all
relationships explicit elders can crowd-source and utilised peer and family knowledge to maintain independence, develop new skills and creatively
engage with friends and family.
20.2.1.5 Design-For-All
Social networks are commonly built with many of the latest Web technologies. These technologies can sometimes race ahead of accessibility
technologies, therefore what principles and approaches are feasible for adapting to and catering for elder users [5][6]?
20.3 Online Social Networking Products
Currently there are a number of venture capital funded commercial players in the social networking market. Often their products are classified as social
media.
To date the largest commercial purchase of a social media company was MySpace for more than $580 million by News Corp. Also of note is that
Facebook received a $240 million investment from Microsoft for a small share of the company, approx. 1.6%.
20.3.1 Commercial
20.3.1.1 Hosted Social Networks
Hosted Social Networks are social network build around attracting a large audience. Membership is usually free, while the companies running the social
networks control the social network's brand. Revenue is often generated through advertising.
MySpace
[16] http://www.myspace.com
Predominately a teen hangout, in 2006 surpassed 100 million accounts.
Members can customize aspects of their profile with HTML.
Well known for the chaotic design of user profiles. Often profiles are brashly visually designed, with no sign of professional visual design.
Ability to personalisation profiles with expressive amateur nature of graphical layouts may lend to its appeal.
Negative aspect is usability and consistency of experience over different profiles.
Facebook
[30] http://www.facebook.com
Initially targeted at university students has continued to extend its appeal to a broader range of age groups.
41
Current market leader.
Profiles more visually consistent.
Ability to extensively enhance profile with many available applications that become part of a mebmers profile
LinkedIn
[31] http://www.linkedin.com
A social network for aimed at professionals.
Focused on enabling people in industry to connect.
Includes ability to do formal introductions between members.
Profile configuration is less extensible than the likes of Facebook and MySpace.
Eons
[32] http://www.eons.com
VC funded (approx. $32 million) social network aimed at elders: Eons.com is the online community for "BOOMers" those of us born between 1946 and
1964 and beyond, who want to learn and do more to make the most of every stage of life. Our community is the place for you to explore your passions
and interests, keep in touch with friends and family, connect with interesting people to share life experiences, and most of all - have fun!</font>
Provides standard social network features and emphasized was aimed at elder market.
Late last year was apparently laying off a large number of staff [16].
BOOMJ
[33] http://www.boomj.com
A social network targeted at an older audience, specifically "Baby Boomers & Generation Jones?.
On top of normal social network functionality has added ability to shop for products and take part in a points reward scheme.
Carries extra non-user generated content in the form of articles about Health, Movies, Finance, Lifestyle, Politics, etc.
20.3.1.2 White Box Social Networks
White Box social networks are a commoditization of social networks into software products. These software products enable interested groups and
companies to setup and run their own branded social networks.
Social network software is either sold as a software package, which can be installed and run on servers, or as with "Software As A Service" model. In
the ?Software As A Service? model an independent company operates and runs multiple social networks; as a service for other companies.
Key players in the white box social network market include:
• Ning
http://www.ning.com
• KickApps
http://www.kickapps.com
• Pluck
http://www.pluck.com
• Pringo
http://www.pringo.com
• SocialEngine
http://www.socialengine.com
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20.3.1.3 European Social Networks
Main companies and startups in the European market, by country where available are:
• (Elder Focused) Germany: Platinnetz
http://www.platinnetz.de
• (Elder Focused) Britain: Saga Zone
http://www.sagazone.co.uk
• European: Badoo
http://www.badoo.com
• UK: Faceparty
http://www.faceparty.com
• Netherlands: Hyves
http://www.hyves.nl
• Germany: StudiVZ
http://www.studivz.ne
• Bulgaria: Aha.bg
http://www.aha.bg
• Denmark: Arto.dk
http://www.arto.dk
• France: Skyrock
http://www.skyrock.com
• Spain: Tuenti
http://www.tuenti.com
• Poland: Grono
http://grono.net
• Belgium company: Netlog
http://www.netlog.com
• French company: Dailymotion
http://www.dailymotion.com
• Worldwide (started in Finland): Habbo Hotel
http://www.habbo.com
20.3.1.4 Noteworthy Rest of World Social Networks
Below are some of the social networks that either dominate a region or country, or which have a large worldwide membership:
• Japan: Mixi
http://www.mixi.jp
• Russia: Vkontakte
43
http://www.vkontakte.ru
• Korea: Cyworld
http://www.cyworld.com
• Brazil, India: Orkut (owned by Google)
http://www.orkut.com
• Asia: Friendster
http://www.friendster.com
• Latin America: Sonico
http://www.sonico.com
• China: Xiaonei
http://www.xiaonei.com
• Hi5
http://www.hi5.com
• bebo
http://www.bebo.com
• Tagged
http://www.tagged.com
20.3.1.5 Niche Social Networks
As part of the segmentation of the social network space many networks are emerging based around shared interests and hobbies. Below are a few
examples:
• Traveling: WAYN
http://www.wayn.com
• Genealogy: 'Geni
http://www.geni.com
• Knitting & Crochet: Ravelry
http://www.ravelry.com
• MyChurch
http://www.mychurch.org
• Books: LibraryThing
http://www.librarything.com
• Art: DeviantART
http://www.deviantart.com
• Movies: Flixster
http://www.flixster.com
• Photoblogging: Fotolog
http://www.fotolog.com
• Music: Last.fm
44
http://www.last.fm
• Learn Languages: Livemocha
http://www.livemocha.com
20.3.1.6 Miscellaneous
PHPizabi
[51] http://www.phpizabi.net
Most interesting to CAPSIL as it is Open Source platform for setting up and running a social network.
Interesting because may be extensible and adaptable so could be perfect for building a research network.
Has standard set of functionality.
20.3.2 Academic
European projects with potential relevance to social networks:
Project
Potential Relevance
AALiance http://www.aaliance.eu
Ambient Assisted Living technologies and approaches can be integrated into social
networks to enable peer and family monitoring.
Accessible http://www.accessible-project.eu
Enable automatic analysis of usability of social networks / Web 2.0 technologies.
AEGIS http://www.aegis-project.eu
Could indirectly identify elder user interface needs and interaction models for social
networks. Indirect since modeling user with visual, hearing, motion, speech and cognitive
impairments.
agent-DYSL http://www.agent-dysl.eu
Though aimed at children with dyslexia could have implications for enabling elders with
dementia access and contribute to social networks, which are primarily textual.
Aladin http://www.ambient-lighting.eu
Contributions on Ageing Friendly Interfaces and Assistive Technologies for the Ageing
Society enables creation of social network interfaces to suit varying elder related
capabilities.
ASK-IThttp://www.ask-it.org
Less obvious applications for Social Networks, but may have relevance if travel and
location information becomes available via an elder's social networks, i.e. publish on
their profile in real-time where they are.
BRAIN& TOBI& TREMOR
Application of BCI's to social networks could be useful for social gaming, especially
where elders are physically impaired. May also be useful for controlling interaction with
social networks, e.g. navigate menu structures.
Bridget IT
Focus on life long learning and multicultural concerns may help enhance appeal of social
networks to diverse group. Could be especially useful in helping understand why different
social networks have broader appeal in different parts of the world.
Cogain http://www.cogain.org
Eye tracking can be useful for evaluating the effectiveness of interfaces designed for
elders, along with potentially enabling elders with physical impairments to interact with
social networks.
CogKnow http://www.cogknow.eu
Opportunity to design a cognitive prosthetic for enhancing elders social connectedness
and engagements on social networks.
CompanionAble http://www.companionable.net
A smart home and robotic companion could be integrated with elder's social network
profiles to enhance the functionality and richness of both. Benefits could be gained
through enabling peer's and friends to remotely engage with the robot, creating a mixed
virtual / physical friend presence.
Confidence http://www.confidence-eu.org
Algorithms for detecting abnormal events or unexpected behaviours in outdoor and
indoor locations used by elders. Might be possible to building upon for detecting
abnormal events social networks.
CWST http://cwst.icchp.org& Dfa@eInclusionhttp://www.dfaei.org Outcomes from Design-for-all conferences, workshop and seminars on eInclusion could
help understand what are appropriate interaction designs and aims for elder social
networks.
Diadem http://www.project-diadem.com
Adaptable of web interfaces for those with reduced cognitive skills and physical skills
could be used to enable a web browser to adapt existing social network interfaces.
Dreaming
Home assistance and eInclusion for independent living, esp. for health and safety
monitoring. Potentially Decision Support System could be deployed to decide which
person to automatically contact in a elder's social network for non-emergencies.
DTV4ALL
Second-generation digital television technologies and algorithms could be extremely
useful in enhancing access to multimedia content in social networks. Many social
network members share video online during socialization.
eAbilities http://www.eabilities-eu.org
45
Outcomes framework about ICT accessibility in home, vechicle and working
environments could help refine how to deploy social networks in non-traditional settings,
e.g. in car, while on the move, etc.
EasyLine+ http://www.arenque-ks.com/easynet
White goods (as in refrigerator or an oven) augmented for elder access and automatic
assistance. May be beneficial to integration into a social network, for example could
white goods could learn normal usage within an elder's social network by examining how
individuals in that network normally use the white goods.
ElderGames http://www.eldergames.eu
Gaming for elder to improve cognitive skills and quality of life would be very useful in the
context of social networks. Could be used to reinforce connections, socialization and
interaction between elders, while presenting opportunities to beneficially game.
Enable http://www.enable-project.eu
A user-centered enabling system, capable of running on a mobile platform, which is
capable of mitigating the effects of disabilities. Techniques for reducing the impact of
disabilities could prove beneficial for maintaining and building social ties.
ePal http://www.epal.eu.com&
eSangathanhttp://www.esangathan.eu
Focus on collaborative working environments for an ageing work force could inform how
to develop social networks for business and job needs of elders.
EU4All http://www.eu4all-project.eu
Life long learning. How could peer to peer learning be enhanced with social networks?
HANDS
Project aimed at helping teenagers with autism diagnosis become better integrated in
society. Could it also help elders in cognitive decline become integrated with social
networks?
HaptiMap http://www.haptimap.org
Haptic, Audio and Visual Interfaces for Maps and Location Based Services. Could be
used to map out non-physical spaces, such as online networks, then enable interaction
with non-physical maps.
HearCom http://www.hearcom.org
May enable elders to self-monitor or peer monitor (on social networks) changes in
hearing abilities. Could also enable automatic adaption of media in social networks to
individual function, e.g. automatic shift frequencies to improve clarity.
Hermes http://www.fp7-hermes.eu
Cognitive aid could provide conversation support, acitivity memory, cognitive training and
episodic memory, e.g. help an elder remember what Paul last did on his SNS profile.
ICT for ALL http://www.ictforall.info
Develop measurements for monitor adaption and benefits of social networks for elders.
MPower http://www.sintef.no/mpower
Middleware for developing and deploying services for people with cognitive disabilities
and elders. Could be used to make it easier for social networks to be integrated with
Smart Home environments.
Oasis http://www.oasis-project.eu
Could enable seamless technical integration between different social networks, services
and digital artifacts, such as a mobile phone, handheld map, etc.
Persona http://www.aal-persona.org&
Sopranohttp://www.soprano-ip.org
Platform that could be used to integrated assisted living environments and technologies
with social networks, e.g. update profile with appropriate caregiver actions.
Replay
Gaming to enable young offenders to learn how to reintegrate. Could be used to help
elders learn how to richly integrate with society based on their changing cognitive and
physical capabilities.
Senior http://www.seniorproject.eu
Examining the social, ethical and privacy issues surround ICT and ageing could be
applied to social networks to aid protecting elder privacy while enable social sharing.
Share-It http://www.ist-shareit.eu
Create next generation assistive devices. Might be useful in helping develop and explore
the feasibility of next gen devices for facilitating social network interaction, e.g. haptic
surfaces.
T-Seniority http://tseniority.ideikon.com&
Vitalhttp://www.ist-vital.org& VMhttp://www.vitalmind-project.eu
Accessing health and social care in an ageing population via TV. Could enable
development of a social network box that elder can plug into their TV to easily access
social networks and related content streams, including meeting up in social networks and
playing fitness games together.
UMSIC
Support potentially marginalized children through music. May translate to elders,
especially idea of positive affecting emotional state through successful navigation and
use of social networks.
VAALID
Virtual Reality and Augmented Reality technologies may be useful for social networks, by
enabling elders to interact with social networks in a "natural / familiar" manner, such as in
3D spaces.
WAI-Age http://www.w3c.org/WAI/WAI-AGE
W3C accessibility Initiative around "Ageing Education and Harmonization" definitely very
useful for creating social network interfaces focused on elder usability.
20.4 Viability of Social Networks for Elders
Online social networks targeted at elders raise questions and concerns:
1. Will social networks just targeted at elders succeed? Do social network thrive on a mixed range of ages and diversity of interests?
2. Are there unique design requirements for social networks for elders that the likes of Eons.com didn't consider?
3. If designing a social network for elders what do elders want in a social network? For example are there beneficial opportunities for an elder to
share their career's activity with their family on an ongoing basis?
4. Can existing social network models simply be transferred from one demographic to another with little in the way of adaption to target groups?
5. Do elders require a pre-created content stream to share in social networks or can they also be creators of user-generated content?
6. Social networks do not exist in isolation from the rest of the Internet. Much of the content on them is user generated and stored elsewhere,
e.g. on YouTube.
46
7. Social network may require a certain network size to carry out meaningful research, therefore setting up a social network for elders research
may fail due to lack of significant membership?
20.4.1 Players/Stakeholders
Issues around privacy, both for the elder and those who interact with elders are a major concern with social networks.
What right to privacy does the caregiver have? What right to privacy does the caregiver?s actions have when those actions directly impact upon the
elder? This is relevant because a social network may share the points of interaction between an elder and those the elder interacts with.
If an elder does share their caregiver?s actions, in a manner such that other?s can comment on those actions ? what right to response does the
caregiver have?
20.4.1.1 Policy Issues
Business
Many social networks are isolated from each other. A profile on one network is completely separate from a profile on another. This has numerous
disadvantages and increases the complexity of participation in social networks.
Disadvantages include an inability to carry out Friending across networks and share profile updates across networks. These disadvantages mean
everyone must belong to the same network to interact in a social network, even if different people find some networks easier to use than others. Another
disadvantage is an ability to migrate between social networks. For example if a social network is failing members cannot simply have all their profile
updates, friend connections, comments and photographs migrate to a new network. Another facet of this is if many family members or friends take part
in a few different social networks, then a person must maintain multiple profiles.
This is advantageous for businesses as it leads to circles of friends joining the same network.
Recently there have been moves towards creating standards around profiles and friend networks, e.g. Friend of a Friend (FOAF) Project [26] and Open
Social [27].
Government
Protection of privacy, with appropriate enforcement of privacy laws.
Protection of right to and ownership of data, i.e. users of social networks may not always retain ownership of the data and media they publish on social
networks. A number of attempts have been made by social networks to add clauses to the membership agreements where the social networks obtain
ownership of user generated content.
Somatic surveillance, as mentioned on page 22 of [17], will need to be addressed to prevent abuse of the data people publish, or devices which monitor
them publish, on social networks.
Social Constraints
What benefits do social networks offer over existing offline social networks? Are their stigmas associated with publishing personal information online?
Possibility of elders in social networks becoming targeted by sophisticated online criminal rings. Identify theft and impersonation becomes easier where
people publish and share what has been previously private information. How can elder?s be educated about safe online practices?
20.4.1.2 Technology Issues
Actuation & Body Sensing & Environmental Sensing Capturing data for profile updates with sensors. Enabling social networks users to indicate what
information to share in raw or filtered form.
Communication R.F. & Firmware Do devices speak directly to social network API?s or go through a middle layer? Danger of speaking directly to social
network API?s is rapidly evolving nature of social networks.
Middleware Middleware could act as privacy guardian by filtering data before it reaches the social network, rather than filter on the social network.
Reduces risk of exposure of private data.
Could be designed to act as translation layer so can communicate with multiple simultaneous social networks.
Software Capabilities to communicate with Web 2.0 technologies and function in restrictive sandboxed environments.
47
Applications that extend social networks often have various security restrictions to protect users. For example a malicious application could copy spam
everyone in a friend?s network.
Services Who and what hosts applications written to run on social networks? The authors of the application or the entity running the social network?
Mobile Aggregation Multiple paths to access the content on social networks to suit device capabilities. For example RSS feeds of profile updates, which
can be accessed by third party web based applications, such as Google?s feed reader.
Security Ensuring privacy of all data.
Data Storage Social networks often have massive data warehousing demands, as millions of users upload photos, make comments, etc.
Reliability Standard issues around very large-scale websites, e.g. distributed redundancy, peak loads, response times, etc.
Communication Networks Interoperability between social networks, as previously mentioned Open Social and FOAF are attempting to tackle some of
the issues.
20.4.1.3 Interaction Issues
Actuation: Screens/Designs/Communication Styles Degradable interfaces and interaction techniques, i.e. interfaces and interaction models that are
robust enough to work with different cognitive and physical abilities. For example don?t design system that require quick feedback, such as a keypress
within a certain time frame.
Design: Physical Detc Currently social networks are accessed via standard computers. Are their affordances and opportunities to redesign social
network interfaces to improve the usefulness and accessibility for elders, e.g. Jive [28]?
Compliance Are a set of accessibility standards required for social networks, or are existing W3C / Design-for-all web standards suffice? A possibility
opportunity for improvement could be enabling users to choose to have core functionality presented in a consistent menu structure and visual style
across multiple social networks, i.e. decouple interface functionality from interface layout.
Dependancies Social networks depend on friend actions. For example my profile gets updated when one of my friends updates their profile. How can
dependancies between friends actions and what updates will or should occur be made clearer? For example if an elder asks a friend a question how do
they know whether their question will be public, public only to friends, or a private communication between two people?
20.4.1.4 Environmental
Home & Clinical
If there is integration between social networking and technologies, such as ambient health monitoring, standard questions arise about energy use,
network communication efficients, etc.
20.5 References
1. ENURGI, "Online marketplace to find and manage your caregivers" http://www.enurgi.com
2. SNAP for Seniors, "Senior Housing, Assisted Living, Nursing Homes, Independent Living" http://www.snapforseniors.com
3. I-neighbors, "Your neighborhood's home on the internet", http://www.i-neighbors.org
4. Dara-Abrams, B. "Toward a Model for Collaborative Gerontechnology: Connecting Elders and Their Caregivers",
5. Gregor, P., Newell, A., and Zajicek, M. Designing for dynamic diversity: interfaces for older people. In Proceedings of the Fifth International ACM Conference on Assistive
Technologies, Edinburgh, Scotland, 151-156, 2002.
6. Hanson, V., and Crayne, S. Personalization of Web browsing: Adaptations to meet the needs of older adults. Universal Access in the Information Society. Universal
Access in the Information Society y, 4(1):46-58, 2005.
7. Glass, T.A., Mendes deLeon, C., Marotta, R.A., and Berkman, L.F. Population-based study of social and productive activities as predictors of survival among elderly
Americans. British Medial Journal (1999), 319:478-483.
8. Hyyppa, M.T. and Maki, J. Social participation and health in a community rich in stock of social capital. Health Education Research (2003), 18(6):770-779.
9. Estelle, J. J., Kirsch, N. L., and Pollack, M. E. Enhancing Social Interaction in Elderly Communities via Location-Aware Computing. CHI 2006 Workshop on Designing
Technology for People with Cognitive Impairments.
10. Morris, M., Lundell, J., and Dishman, E., Catalyzing social interaction with ubiquitous computing: a needs assessment of elders coping with cognitive decline, CHI 2004
Extended Abstracts on Human Factors in Computing Systems, 2004, 1151-1154
48
11. Lindlay, S., Harper, R., and Sellen, Ab. Designing for Elders: Exploring the Complexity of Relationships in Later Life, HCI2008, Liverpool.
12. Sung, M., Marci, C., and Pentland, A. Wearable feedback systems for rehabilitation. Journal of Neuroengineering and Rehabilitation, June 2005, 2:17
13. boyd, d. m., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), article 11.
http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html
14. boyd, danah. Why Youth (Heart) Social Network Sites: The Role of Networked Publics in Teenage Social Life. MacArthur Foundation Series on Digital Learning - Youth,
Identity, and Digital Media Volume (ed. David Buckingham). Cambridge, MA: MIT Press
15. boyd, danah. Bibliography of Research on Social Network Sites, http://www.danah.org/SNSResearch.html
16. Read Write Web's article "Why Eons, a MySpace for Old People, Failed", http://www.readwriteweb.com/archives/eons_a_myspace_for_old_people.php
17. Ethics of e-Inclusion of older people, Discussion paper for the Workshop on Ethics and e-Inclusion, Bled, May 2008
18. Farrugia, M. and Quigley, A., Visual data exploration of temporal graph data, Conference on Visualization and Data Analysis 2009, Part of IS&T/SPIE's International
Symposium on Electronic Imaging 2009, 18-22 January 2009, San Jose, California, USA
19. Viviane Reding, "Social Networking in Europe: success and challenges", Safer Internet Forum, Luxemborug, 26 September, 2008
20. U.K. Social Networking Site Usage Highest in Europe, http://www.comscore.com/press/release.asp?press=1801
21. Social Networking Explodes Worldwide as Sites Increase their Focus on Cultural Relevance, http://www.comscore.com/press/release.asp?press=2396
22. Social Networking Goes Global, http://www.comscore.com/press/release.asp?press=1555
23. ARGNet, Alternative Reality Gaming Network, http://www.argn.com
24. World Map showing popularity of social networks based on web traffic, http://www.oxyweb.co.uk/blog/socialnetworkmapoftheworld.php
25. Pipl Statistics, http://www.pipl.com/statistics
26. Friend of a Friend (FOAF) Project, http://www.foaf-project.org
27. Open Social, http://www.opensocial.org
28. Jive, social networking for your gran, http://jive.benarent.co.uk
29. MySpace, http://www.myspace.com
30. FaceBook, http://www.facebook.com
31. LinkedIn, http://www.linkedin.com
32. Eons.com, http://www.eons.com
33. BOOMJ, http://www.boomj.com
34. Ning, http://www.ning.com
35. KickApps, http://www.kickapps.com
36. Pluck, http://www.pluck.com
37. Pringo, http://pringo.com
38. SocialEngine, http://www.socialengine.net
39. Platinnetz, http://www.platinnetz.de
40. Habbo Hotel, http://www.habbo.com
41. Badoo, http://www.badoo.com
42. Faceparty, http://www.faceparty.com
43. Netlog, http://www.netlog.com
44. Dailymotion, http://www.dailymotion.com
45. Hyves, http://www.hyves.nl
46. StudiVZ, http://www.studivz.ne
47. Aha.bg, http://www.aha.bg
48. Arto.dk, http://www.arto.dk
49. Skyrock, http://www.skyrock.com
50. Tuenti, http://www.tuenti.com
51. Grono, http://grono.net
52. PHPizabi, http://www.phpizabi.net
53. Mixi, http://www.mixi.jp
54. Vkontakte, http://www.vkontakte.ru
55. Cyworld, http://www.cyworld.com
56. Orkut, http://www.orkut.com
57. Friendster, http://www.friendster.com
58. Sonico, http://www.sonico.com
59. Xiaonei, http://www.xiaonei.com
60. Hi5, http://www.hi5.com
61. Bebo, http://www.bebo.com
62. Tagged, http://www.tagged.com
63. WAYN, http://www.wayn.com
64. Geni, http://www.geni.com
65. Ravelry, http://www.ravelry.com
66. MyChurch, http://www.mychurch.org
67. LibraryThing, http://www.librarything.com
68. DeviantART, http://www.deviantart.com
69. Flixster, http://www.flixster.com
70. Fotolog, http://www.fotolog.com
71. Last.fm, http://www.last.fm
72. Livemocha, http://www.livemocha.com
49
21 Activity Monitoring
21.1 General Description
Aging is undoubtedly one of the key global challenges that is exuberated by changing demographics and improving effectiveness in therapeutic
interventions. Despite the successes of the reactive model of care the aging process threatens the elders? quality of life. In contrast, continuous
monitoring of elders has the potential of providing proactive model of care.
Although the process of aging is associated with a decline of many physical and cognitive functions, an elder can maintain independence and high
quality of life provided that the individual is able to do activities of daily living (ADL), instrumental activities of daily living (IADL), challenging physical and
mental exercises, and maintains his social activities and engagement. In fact, functional impairment is sometimes defined as difficulty performing, or
requiring the assistance of another person to perform ADLs or IADLs [Ref: Michigan] [McDowell,96, Rialle et al., 2008]. Needless to say, early detection
of changes in the elders? activities is critical in providing appropriate level of care. In addition, changes in activities are likely to reflect important changes
in their cognitive, sensory-motor (e.g., balance) or physical competence and as such may reflect symptoms of neurodegenerative diseases. One can
easily think of scenarios in which the application of technological advances to the care of elders has the potential to restore his quality of care as well as
quality of life.
Because of the importance of elders? ADL competence, clinicians and caregivers adopted a formal, albeit subjective, approach to the assessment of
activities [Diel et al, 2005] [Patterson and Mack, 2001]. As such this judgment is subject to a variety of biases, including those due to contextual effects
on sampling and denial. By the contextual effects of sampling, we mean biases that arise when an elder knows that he is being observed and that the
outcome of this observation may lead to the loss of independence and possible move to the nursing home. Of course, infrequent sampling will distort the
assessment interpretation of the data due to the variation in performance faster than sampling frequency ? effect referred to as aliasing in the
information communication community. Sufficiently frequent or continuous assessment, therefore, not only reduces the biases, but also allows the
assessment of variability.
Continuous assessment of elders? activities will most likely be achievable using technology that is unobtrusively integrated within the normal living
environments such as the elders? houses or residential facilities. In order to achieve the ability to detect and classify the elders? activities, the
unobtrusive monitoring system must be capable of collecting data from a suite of distributed sensors combine them with appropriate models and
process them with a set of inference algorithms, and utility-based decision-making processes
21.1.1 Issues
There are numerous issues that need to be tackled before we have systems capable of automatic classification and assessment of human activities.
• Complexity: Human activities are complex and sometimes difficult to classify even by human observers.
• Individual Differences: Each elder may have idiosyncratic ways to execute even the simplest actions, so that any system developed for a
populations must be adapted to the individual?s behaviors.
• Reliability: Unobtrusive or minimally obtrusive sensing renders the inference even more difficult because the indirect observations are usually
more noisy and unreliable.
• Deviations from Normal: It is impossible to train on deviations and therefore these must be defined as incongruent events Ref
• Maintenance: In order to assure economic feasibility for the activity monitoring system, it must be very robust and require minimal
maintenance
• Installation: The system must be easy to install by an informal or low-skill formal caregiver.
• Power: The battery in any battery-powered devices must last at least six months. This may be achievable using intelligent power management
or power harvesting techniques.
• Ground Truth: In real-life situations it is very difficult, if not impossible to obtain the label for the actual activity performed by the observed elder
• Stakeholder Differences: Each stakeholder may have different requirements for the monitoring and classification system.
21.1.2 Problems and Challenges
• [Add Notes Here]
50
21.2 Justification
The currently accepted approach to the assessment of the ADLs and IADLs is based on subjective judgment of the formal and informal, e.g., family,
caregivers, even of the methodology is formalized [Patterson and Mack, 2001]. As such this judgment is subject to a variety of biases, including those
due to contextual effects on sampling and denial. By the contextual effects of sampling, we mean biases that arise when an elder knows that he is being
observed and that the outcome of this observation may lead to the loss of independence and possible move to the nursing home. Of course, infrequent
sampling will distort the assessment interpretation of the data due to the variability in performance ? effect referred to as aliasing in the information
communication community. On an intuitive level, aliasing is a misinterpretation of the observed data that occurs when the sampling rate is slower than
necessary to capture the variability in the observed phenomenon. More frequent or continuous assessment, would therefore not only reduce the biases,
but also allow assessment of variability.
The shortcomings of the current approaches to the assessment of ADLs and IADLs, therefore, offer an ideal opportunity to introduce technological
solutions for frequent or continuous monitoring and assessments of ADLs and possibly a subset of IADLs.
21.3 Scientific Basis
21.3.1 Technical Approaches to ADL Assessment
This section provides a brief overview of a subset of approaches previously used for inferencing and classification of ADLs based on a variety of
technological and algorithmic approaches. One of the firsts studies of ADLs using objective monitoring techniques were performed by Togawa and his
colleagues [Yamaguchi, 1998], who monitored several daily activities of the subject (sleeping hours, toileting, meals) as well as a number of
physiological parameters. Since then, there have been multiple attempts to develop techniques for inference and classification of ADLs based on
different technologies and yielding varying degree of success.
21.3.2 Sensors and Hardware
The technological approaches range from unobtrusive passive infrared sensors to more complex passive or active radio-frequency identification (RFID)
systems based on unique tags attached to most of the relevant objects.
21.3.3 Algorithms and Software Systems
The algorithms range from aggregation and visual representation ? visualization ? to sophisticated probabilistic methods. As it turns out even simple
depiction of the sensor activities can be very effective for exploratory analysis but the desired inferences do require statistical data processing, pattern
recognition and classification.
21.3.4 Evaluation
Although there are ever increasing efforts to develop techniques for monitoring of activities, there is surprising small number of studies of these systems
in elders? homes. There are several empirical studies that attempt to evaluate the sensor systems in laboratory environments, [Abowd and Mynatt,
2004; Doctor, Hagras, Callaghan, 2005; Helal, Mann, et al, 2005]
Although the ultimate evaluation requires monitoring for a substantial length of time, there are results from short term studies, e.g., 13 day installation,
Glascock and Kutzik, 2000. The system was used to assess wakeup time and medication tracking using simple statistical analysis. An alternative
approach has been attempted by the ILSA project of Honeywell.
21.3.5 Ethical and Social Aspects
Although the advantages of monitoring systems applied to care for elders is quite apparent there are numerous ethical issues associated with such
systems. These issues range from security and privacy to the social and psychological implications of machine-based care delivery. Although there is
increasing effort to address these issues, preliminary results of these studies suggest that the actual decisions to use such systems by the elders will
depend on the case-specific cost-benefit tradeoffs.
The following is a preliminary collection of a sample of academic and industrial organizations involved in research, development and implementation of
activity monitoring systems.
51
21.4 Research
21.4.1 Academic Institutions
Bath Institute of Medical Engineering (UK)
Curtin University of Technology Centre for Research on Ageing (AU)
Indiana University Center on Aging and Aged (US)
Lancaster University Institute for Health Research (UK)
MIT AgeLab (US)
• Recognizing Activities of Daily Living in the Home Setting using Ubiquitous Sensors.
• Emmanuel Munguia Tapia, Stephen Intille, Kent Larson, Pallavi Kaushik
Carnegie Mellon/University of Pittsburgh Quality of Life Technology Center (US)
University College Dublin Center for Technology
• Research for Independent Living (IE)
University of North Carolina Institute on Aging (US)
Oregon Health & Science University (US)
• Unobtrusive monitoring of activity and mobility using passive infrared sensor and contact switches. Ongoing studies with almost 300
individuals.
Duke University (US)
• Smart home program
News article: University of Rochester, New York (US)
• Smart Bandage, Smart home
Journal article: University of Texas at Arlington (US)
• MavHome: An agent-based smart home project. Cook (2006)
University of Virginia (US) (PDF File)
• Authors propose a system architecture for smart health care based on an advanced Wireless Sensor Network (WSN). It specifically targets
assisted-living residents and others who may benefit from continuous, remote health monitoring.
University of Virginia (US)
• Home Guardian sensors are designed to feed information into a PC, which transmits the information to either a monitoring service or
caregiver. This is a project of University of Virginia featuring a detector that uses floor sensors, rather than a device strapped to the body, to
detect when someone falls.
21.4.1.1 Industry
Health Anywhere (Formerly VaaSah)
• Independent Living at Home
HomeFree
• A system for localization, mobility assessment and safety assurance (wandering mitigation) based on active radio frequency identification
(RFID).
Vigil
• A system based on passive RFID
Aipermon GmbH & Co. KG
• AiperMotion is a three-dimensional activity sensor (accelerator) for measuring, recording and motivating everyday activities. Data are
transferred through the AiperCoach home system via Bluetooth which is later retrieved by the program supervisor. Aipermon?s evaluations
52
are then used to generate written responses by mail or email to the user for successful weight and activity management.
ADT/GE: Quietcare System
• Fast-acting alert system: Detects potential health risks , Prevents falls and hospitalization and Protects privacy and dignity.
Continua Health Alliance
• The Continua Health Alliance is an organization dedicated to enabling interoperable health care products and solutions worldwide
• Member companies include: Roche, Novartis, Intel, Samsung, google etc.
Honeywell HomMed
• Device or System: Genesis DM
• Description:Genesis DM is seamlessly integrated into the innovative new Honeywell HomMed LifeStream? telehealth platform, providing
web-enabled, on-demand access to disease-specific symptom management (DSSM), customizable by diagnosis and symptoms. This
tele-health device measures heart rate, blood pressure, and weight, and provides customizable subjective disease-related queries for a more
complete picture of an individual?s health. Automated set up and automatic patient engagement with a friendly voice and easy-to-use
interface guide the patient at every step.
Intel Telemedicine
Carematix
• Device or System: Wellness system
• Description:The CWS provides easy monitoring of the basic wellness parameters via a wireless connection between the monitoring device
and a hub (transceiver) located in the home. The hub transmits the information to the Carematix internet server where the data is added to the
patient's chart.
• Using a web-browser, the caregiver can track the patient's data, graph the results, monitor trends, annotate variances, set alert criteria, and
send reminders and receive alerts via e-mail or pager
Healthsense eNeighbor® Resident Monitoring System
• Provides alerts related to significant changes in ADLs.
News article [edit] Research
* Players
* Projects
* Funding
[edit]
Commercial
*
*
*
*
Products
Players (links to VCs/Angels/Agencies/MNCs/SME)
Procurement
Business Models
[edit] Standards
[edit] Gaps
*
*
*
*
Gaps
Gaps
Gaps
Gaps
in
in
in
in
technology
the basic science
operation
implementation
21.5 References
M.B. Patterson and J.L. Mack. The Cleveland Scale for Activities of Daily Living (CSADL): Its reliability and validity. Journal of Clinical Gerontology, 7,
15-28, 2001.
53
22 Driving Assistance
Assisted driving systems are a type of safety or extra-sensory device that provide various types of support to drivers, ranging from unobtrusive displays
providing useful information to systems that directly control the steering of the vehicle. Part of maintaining an active elderly life is driving. In places where
public transit is well developed, this need may be lessened, but in most locations, the loss of driving privileges can be crushing to an elderly person.
Often, it can bring about feelings of loss of independence.
One excellent example of Driving Assistance systems is that of the Anti-lock Braking System, or ABS, available on most modern cars. By pulsing the
brakes to prevent the locking of wheels, ABS has been shown to greatly reduce stopping distances on most surfaces. These types of systems could
serve elderly drivers by providing support to the areas elderly people often suffer a loss of visual and aural acuity and motor skills.
In general, these systems are broken up in to one of three categories; Passive, Partial Control, and Full Control systems. Essentially, they are broken up
based on how much direct influence they have on the driver. Passive systems simply provide the driver with more information than are normally
receiving, but have no physical actuation. Partial and full control systems on the other hand involve the system directly influencing the maneuvering of
the vehicle, i.e. velocity and steering.
22.1 Issues
The topic of elderly drivers is one often debated. However, the issues involved with assisted driving systems are not only limited to those of elderly
rights. There are a number of concerns associated with assisted driving systems:
• Systems should not actuate the vehicle in such a way that injures the driver. This includes the steering wheel and pedals.
• Some drivers will feel a loss in confidence and perhaps embarrassment at needing the system.
• Some systems may provide real-time tracking of the vehicle for monitoring purposes. Additionally, some systems may monitor health
conditions of the driver. This information should be kept confidential.
22.2 Justification
Under test conditions commercially available Driving Assistance systems have shown to improve driver safety and even reduce the number accidents.
IN PROGRESS
22.3 Research
22.3.1 Projects
Research in Driving Asssitance systems and intelligent transportation systems is being conducted by universities and institutes the world. Some
prototypical exmaples are:
• Kuroki et al. - Multimodal cruising assist system that uses wide angle cameras and an in-dash display to enhance visibility for drivers.
• Kamal et al. - Using fuzzy logic and driver modeling, the system detects/avoids abnormalities in driving as preventive measure against
accidents.
• Mutoh et al. - This system avoids traffic accidents by mutually communicating with other vehicles using camera vision, wireless networking,
and GPS .
• Tsugawa et al. - wireless networking, camera vision, inter-vehicle communication Elderly driver assistance system which assists driver using
cooperative driving between two vehicles.
22.3.1.1 Funding
There are a variety of funding sources and projects currently available in this field. Some examples are:
22.3.1.1.1 USA
22.3.1.1.2 EU
22.3.1.1.3 Japan
• National Institute of Advanced Industrial Science and Technology
• Ministry of Education, Culture, Sports, Science, and Technology
• New Energy and Industrial Technology Development Organization
22.4 Commercial
54
22.4.1 Products
There are a variety of Driving Assistance systems available on modern vehicles, such as ABS, ESC, and TCS. Recently, various systems have been
developed to help avoid collisions or to help mitigate damage when a collision is deemed unavoidable. Common elements involve the priming of braking
systems and the tightening of seat belts just prior to collision.
22.4.1.1 Players
Most major automobile manufacturers are involved in the development of Driving Assistance systems.
22.5 Gaps
22.5.1 In technology
• Wireless networking approaches often require the installation of system specific antenna. The scale required for a viable system makes this
unfeasible. A standard for pan-national networking, or systems that use already existing wireless communication antennas, is needed before
systems requiring wireless networking are realistically possible.
• Vision systems often have a trade-off of speed vs. accuracy. For Driving Assistance systems, both are necessary. Faster hardware as well as
algorithms are required in order for real-time, accurate vision systems to be usable.
• Obstacle detection sensors such as laser range finders and millimeter wave radar have become increasingly accurate, however, there is still
room for improvement. Again, there is a trade-off between speed and accuracy.
22.5.1.1 In the basic science
22.5.1.2 In operation
• Drivers are often unaware of how to use Driving Assistance systems properly, or are lulled into a false sense of security, leading to reckless
driving assuming that the system will cover their mistakes.
22.5.1.3 In implementation
• For systems involving communication between vehicles, it is necessary for all vehicles to have the system implemented.
22.6 Future Vision
The ultimate goal for Driving Assistance systems would be one that not only monitors the attention and condition of the driver, but also monitors
navigation, road condition, and the presence of obstacles. The system could ensure the driver is paying attention to the road and could detect if the
driver is having physical complications. Navigation could be monitored via GPS or wireless networking. Not only could the real-time position be
monitored by loved ones, but the system could notify the driver if they have strayed from a desired course. This could also incorporate updates about
inclement weather or adverse road conditions. Finally, the system would ideally monitor surrounding obstacles and take action to avoid collisions when
possible. In the event a collision is unavoidable, the system would prepare the vehicle for an impact, reducing the risk to the driver.
22.7 References
• [1] http://www.keishicho.metro.tokyo.jp/kotu/kourei/koureijiko.htm (Japanese Only)
• [1] RACV, http://www.racv.com.au/wps/wcm/connect/Internet/Primary/my+car/car+safety/safety+equipment/brakes/ABS/
• [2] Kuroki, Y., Okino, T., Haraikawa, T., Sakane, Y., and Takebayashi, Y. 2007. Multimodal Cruising Assist to Enhance the Drivers' Abilities to
Perceive Surrounding Contexts Using Panoramic Presentation with Dynamic Multiple Windows. In Proceedings of the Fifth IEEE international
Conference on Pervasive Computing and Communications Workshops (March 19 - 23, 2007). PERCOMW. IEEE Computer Society,
Washington, DC, 429-434.
• [3] M.A.S. Kamal, T. Kawabe, J. Murata, and M. Mukai, Driver-Adaptive Assist System for Avoiding Abnormality in Driving, Proc. of the 2007
IEEE Conference on Control Applications, Singapore, Oct. 2007.
• [4] Mutoh, N.; Sasaki, Y.; Kusatani, M., "A Driver Assisting System for Eco-Vehicles with Motor Drive Systems Which Avoids Collision with
Running Vehicles by Using Inter-Vehicle Communications," Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE , vol., no.,
pp.508-513, Sept. 30 2007-Oct. 3 2007
• [5] Shin Kato, Naoko Minobe, Mayumi Kawai, Sadayuki Tsugawa, Driver Assistance System with Cooperation between Vehicles - A Proposal
and Fundamental Experiments -, Proceedings. JSAE Annual Congress S0434A, VOL. 53-05, pg. 7-12 (2005)
55
23 Robotics
23.1 Robots for the Aged Society
The International Organization for Standardization, in ISO 8373, gives the definition of robot as: "an automatically controlled, reprogrammable,
multipurpose, manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation
applications." [1]
There were more than one million robots in operation worldwide in the first half of 2008, with roughly half in Asia, 32% in Europe, 16% in North America,
1% in Australasia and 1% in Africa. [2]
Robots can be roughly classified into two categories; industrial and non-industrial. Non-industrial robots are generally focused on a service, or a job they
are needed to do. The former includes tasks which a robot can do with greater productivity, accuracy, or endurance than a human. Currently, many
factory jobs are performed by robots that enable us to provide cheaper mass-produced good, for instance, automobiles and electronics. Robotic
applications often consist of dirty, dangerous, or dull jobs which humans find undesirable. The expected application fields of such robots include
domestic works, medical surgery, rehabilitation assistance and care service for the aged.
23.2 Issues
The average age of population is increasing in many countries, especially in Japan, meaning that there are more elderly people to care for with fewer
people available to do so. In response to this, there has been a push in the development of rehabilitation robots to support the physical and mental
aspects involved in care for the elderly and disabled. It is envisioned that robots will provide not only a reduction in the cost of providing care, but also an
improvement in the quality of human life.
However, there are concerns involved in robotic care systems:
1. Availability of robots (due to social/financial inequality)
2. Robot are not human, and so a component of danger is possible
3. Acceptance by elderly and disabled people
5. Acceptance by user (caregiver)
6. Technical difficulty (possibility of breakdowns)
7. Trouble related to vested group on developing
23.3 Justification
Population aging is constituted by a shift in the distribution of a country's population towards greater ages. Thus an increase in the population's mean or
median age, a decline in the fraction of the population composed of children, or a rise in the fraction of the population that is elderly are all aspects of
population aging. Many countries in the world are facing aging society as shown table I. We are facing lack of labor and various fields of industry,
especially care service. In order to solve these problems, a robot attracted attention as an alternative. Consequently, such social environment leads a
person to use robot in daily life. Table 1. Dynamics of Population Aging in the Modern World [3] Observed and Forecasted Percentages of the Elderly
(65+ years) in Selected Areas, Regions, and Countries of the World: 1950, 2000 and 2050.
Area 1950 2000 2050
World 5.2% 6.9% 19.3%
Africa 3.2% 3.3% 6.9%
Latin America and the Caribbean
3.7%
5.4%
16.9%
China 4.5% 6.9% 22.7%
India 3.3% 5.0% 14.8%
Japan 4.9% 17.2% 36.4%
Europe 8.2% 14.7% 29.2%
Italy 8.3% 18.1% 35.9%
Germany 9.7% 16.4% 31.0%
Sweden 10.3% 17.4% 30.4%
56
U.S.A. 8.3% 12.3% 21.1%
Source: United Nations 2001.
[3] Gavrilov L.A., Heuveline P. Aging of Population. In: Paul Demeny and Geoffrey McNicoll (Eds.) The Encyclopedia of population. New York,
Macmillan Reference USA, 2003, vol.1, 32-37.
23.4 Scientific Basis/efficacy/evidence)
1. Mechanical engineering??Body??/ Materials, Structure, Control
2. Computer??Brain? /Architecture, Networks
3. Sensor technology??Sense? /Device, Signal processing
4. Information processing??Intelligence? /Recognition, Communication, Interface
23.5 Research projects
Research of service robot for aged people is being conducted by universities and institutes. Some examples are:
1) TWENDY-ONE
TWENDY-ONE is a sophisticated human-symbiotic-robot which equips all the functions described above. The special feature of TWENDY-ONE is the
combination function of the dexterity with passivity and the high-power output. TWENDY-ONE equips high output actuators with the simple mechanical
passive impedance mechanism. When TWENDY-ONE manipulates an object with various shapes, it is easy for TWENDY-ONE to adapt to the object by
passivity to absorb external force generated by the positioning deviation. In the same way, TWENDY-ONE can adapt to human motion and hold a
human. As a result, TWENDY-ONE can manipulate an object dexterously as well as support a human.
2) RI-MAN
Someday, robots could replace humans as nurse's aides, but first they will need a little sensitivity training. Japan's Ri-Man is headed in the right
direction. With sensors that enable it to see, smell and hear its environment, it also has some 320 pressure points on its arms and chest that allow it to
sense the exact position of whatever it's holding. The bot can lift 80 lbs. today, but researchers hope to strengthen the motors in Ri-Man's arms without
increasing their size, so they still resemble those of a man, not a monster.
23.5.1 Players
[4] H.IWATA, S.KOBASHI, T.AONO, T.KOBAYASHI and S.SUGANO: ?Development of 4-DOF Anthropomorphic Tactile Interaction Manipulator with
Passive Joint,? Journal of Robotics and Mechatronics, 2007
[5] Toshiharu Mukai, Masaki Onishi, Tadashi Odashima, Shinya Hirano, and Zhiwei Luo : "Development of the Tactile Sensor System of a
Human-Interactive Robot "RIMAN"", IEEE Transactions on Robotics, Vol.24, No.2, pp.505-512, (2008).
23.5.2 Funding
1. The Robotics Industry Development Council
2. New Enegry and Industrial Technology Development Organization
3. Ministry of Health, Labor and Welfare
4. Industry Science Technology Foundation
5. Shimane Industrial Promotion Foundation (for enterprise only)
23.6 Commercial
23.6.1 Products
1) WAKAMARU
Wakamaru is a Japanese domestic robot made by Mitsubishi Heavy Industries, primarily intended to provide companionship to elderly and disabled
people. ? 2) PARO
57
Recent advances in robotics have been applied to automation in industrial manufacturing with the primary purpose of optimizing practical systems in
terms of such objective measures as accuracy, speed, and cost. However, the resulting robots are mostly kept away from human beings because
people can be injured during their everyday functioning. Unlike industrial robots, ?Mental Commitment Robots? are developed to interact with human
beings and to make them feel emotional attachment to the robots. Rather than using objective measures, these robots trigger more subjective
evaluations, evoking psychological impressions such as ?cuteness? and comfort. Mental Commitment Robots are designed to provide 3 types of
effects: psychological, such as relaxation and motivation, physiological, such as improvement in vital signs, and social effects such as instigating
communication among inpatients and caregivers.
3) PAPERO
The PaPeRo is a personal robot being developed by Japanese firm, NEC Corporation. It is noted for its cute appearance and its facial recognition
system. The robot's development began in 1997 with the first prototype, the R100, and adopted the name PaPeRo, which stands for
"Partner-type-Personal-Robot" in 2001. The PaPeRo has been researched and developed with the intention of its being a partner with human beings
and its being able to live together with them. For this reason, it has various basic functions for the purpose of interacting with people. Here we introduce
the essential elements and functions needed for that interaction.
4) HAL
HAL has developed to expand and improve physical capabilities. The power units are attached on each joint of HAL. The torque of power units are
converted from HAL to wearer's limb through the mold fastening equipments. Potentiometers are attached to the each joint in order to measure the joint
angles. The FRF sensors are embedded into shoes to detect the CoP (Center of Point). The bioelectrical signal sensors are detected to the signals such
as myoelectricity. In addition, a computer and batteries are attached on a wearer's waist, so the wearer can move in stand-alone mode.
5) MY SPOON
Eating is a basic motion for humans. MY SPOON, as meal-assistance robot, assists the physically handicapped person to eat by him/herself.
23.6.2 Players
[11]Namera, K.; Takasugi, S.; Takano, K.; Yamamoto, T.; Miyake, Y.;? Timing control of utterance and body motion in human-robot interaction,? Robot
and Human interactive Communication, 2008. RO-MAN2008. pp. 119-123, 2008.
[12]Wada.K, Shibata.T, ?Robot Therapy in a care house ? Its sociopsychological and Physiological Effects on the Residents-,? International Conference
on Robotics and Automation (ICRA2006), pp.3966-3971. 2006. [13] Sato, M.; Sugiyama, A.; Ohnaka, S.;? Auditory system in a personal robot,
PaPeRo? Consumer Electronics, 2006. ICCE?06. pp.19-20, 2006.
[14] Hayashi, T.; Kawamoto, H.; Sankai, Y.;,? Control method of robot suit HAL working as operator's muscle using biological and dynamical
information?, Intelligent Robots and Systems, 2005 (IROS2005). pp.3063-3068. 2005.
[15] Zhang, Xiu; Wang, Xingyu; Wang, Bei; Sugi, Takenao; Nakamura, Masatoshi;,? Real-time control strategy for EMG-drive meal assistance robot ?
my spoon,? Control,Automation and Systems, 2008. ICCAS 2008. pp.800-803. 2008.
23.6.3 Procurement
23.7 Standards
The ISO/TC184/SC2 decided to begin standardizing industry robots, including service robots, in a Paris meeting (Paris, July 2006). The standard for
safety of robots started in October 2006.
23.8 Gaps
Many researches have been studied to adapt to daily life. However, there are few robots that were used practical due to the following reasons: 1.
Miniaturization 2. Saving energy 3. Cost 4. Limited function
Scientific Bases on
1. Accuracy 2. Reliaility 3. Flexibility
Issues on
58
4. Cognitive Science 5. Psycology 6. Brain science
A number of sensors which are able to sense its environment suffer from drift and error by environmental conditions. Consequently, such factors gave
us a feeling of insecurity. Besides, mostly robots do not have the second best policy when a robot could not acquire a desirable data at each sensor.
After all, it makes robot to stop, stumble, or do unexpected action.
A robot deals faithfully with command by humans or preprogram. We are able to predict the problem of robot as follows: 1. It is difficult to accomplish
parallel assigned tasks at same time. 2. Many robots can not cope with unforeseeable circumstance. 3. The order of priority in robot should regulate
depend on situations.
23.9 Future Vision
The development of human symbiotic robots that can support human daily activities is greatly expected to be a measure against labor shortages in
aging societies. A robot should become intelligent, including emotion and intention of humans, and have multi-functions and ability of sensing
environment reliably. Furthermore, it is desirable for shape of robot to be familiar and user-friendly. Although there are many problems when the robot
supports humans in daily life, it becomes one of alternative in aging society. In order to do that, the positive perception of the robots is indispensable.
Finally, a robot and humans would be symbiotic relationship.
23.10 References
[1] "Definition of a robot" (PDF). Dansk Robot Forening. Archived from the original on 2008-07-15. Retrieved on 2007-09-10.
[2] Robots Today and Tomorrow: IFR Presents the 2007 World Robotics Statistics Survey". World Robotics (2007-10-29).
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24 Continence
60
25 Smoking Cessation
The rationale for smoke free laws is to protect people from the effects of second-hand smoke, which include an increased risk of heart disease, cancer,
emphysema, and other diseases. Laws implementing bans on indoor smoking have been introduced by many countries in various forms over the years,
with some legislators citing scientific evidence that shows tobacco smoking is harmful to the smokers themselves and to those inhaling 'second-hand
smoke'. In addition, such laws may lower health care costs, improve work productivity, and lower the overall cost of labor in a community, thus making a
community more attractive for employers. The World Health Organization considers "smoke-free laws to influence a reduce demand for tobacco by
creating an environment where smoking becomes increasingly more difficult and to help shift social norms away from the acceptance of smoking in
everyday life". Along with tax measures, cessation measures, and education, smoking ban policy is currently viewed as an important element in lowering
smoking rates and promoting public health. When correctly and strictly implemented it is seen as one important policy agenda goal to change human
behavior away from unhealthy behavior and towards a healthy lifestyle.
On March 29, 2004, the Irish Government implemented a ban on smoking in the workplace, the first country to do so. In Norway similar legislation was
put into force on July 1 the same year. The whole of the United Kingdom became subject to a ban on smoking in enclosed public places in 2007. Many
other EU countries now have smoking bans (either full or partial) and many US states also have introduced smoking bans.
Several studies have documented health and economic benefits related to smoking bans. It was reported in Jan 2009 (1) "In the first 18 months after
Pueblo, Colorado enacted a 2003 smoking ban, hospital admissions for heart attacks dropped by 27% while admissions in neighboring towns without
smoking bans showed no change. The decline in heart attacks was attributed to the smoking ban, which reduced exposure to secondhand smoke. A
similar study in Helena, Montana found a 40% reduction in heart attacks following the imposition of a smoking ban".
25.1 References
• 1. http://www.impactlab.com/2009/01/01/drop-in-heart-attacks-due-to-smoking-ban/
• Back to Government Policy
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26 Sensors
26.1 Overview
A key capability of any assisted living technology system is the ability to sense. This can take the form of direct measurement of biometric parameters
e.g. ECG (electro cardiograph) or indirectly through tracking a person's interaction with their physical environment e.g. movement using accelerometers.
Research in recent years has presented the concept of the body sensor network BSN which individual sensors connected together normally via wireless
network such as 802.15.4 are used to deliver a comprehensive view of a person's state of wellbeing. The majority of wireless sensor platforms share a
common set of system components:
• Microcontroller - Provides the computational capabilities to the platform.
• Communications - Provides low power wireless communications
• Sensor interfaces - hardware interfaces to external sensor boards
• Memory - External memory e.g. mirco SD card
• Power Supply - e.g. Lithium ion battery.
• Sensing e.g. acceleration
• Development Environments
26.2 Key Features
Ren et al described the key features of WBSN's for medical applications as follows:
Reliability
Biocompatibility
Portability
Privacy and security
Light weight protocols
Retrievability
Energy aware communication
Prioritized traffic
RF radiation safety
26.3 Sensor Nodes
Academic
Commercial
• ACme
• Ambient Systems
• BEAN project
• BSN Node
• Cortex Project
• DSYS25
• eyesIFXv1
• Eco
• eyesIFXv2
• FireFly
• Glacsweb
• Hoarder Board
• Marsian
• MIThril
• Particles
• Porcupine
• SensAction-AAL
• ScatterWeb
• SquidBee
• TinyNode 584
• Tmote mini
• Tyndall Mote
• T-Node
• Wireless Integrated Network
Sensors (WINS)
• WiseNet
• Accsense
• ANT
• Atlas
• BTnode rev3
• CRICKET
• Ember
• EnOcean
• Fleck
• IMOTE2
• IRIS
• Kmote
• L-Node
• MICA2
• MICAz
• MicroStrain Sensors
• Newtrax Wireless
Mesh Nodes
• PicoCricket
• SHIMMER
• SmartMesh
• Sun Spot
• Tip Mote
(MTM-CM5000-MSP)
• TMote Invent
• TELOSB/TMote Sky
• WeBee
• WSN430 sensor node
26.4 Design Considerations
• Design Aspects of Body Sensor Networks
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26.5 Resources
• Reports
• Books
• Publications
26.6 References
• The Sensor Network Museumtm
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27 Data Processing
The use of technology to aid independent living has the side effect of generating potentially vast amounts of data. How that data is managed, processed,
and applied is crucial to the successful deployment of this technology. In terms of data processing there are many aspects that need to be considered
such as the best approach to process the data, i.e., on board data processing verses streaming to a centralised processing node. This includes
identifying optimal solutions in terms of CPU overhead, battery life performance, processing time, etc. In addition, data mining techniques need to be
developed to allow the identification of events that require monitoring or intervention. Recommendations will be developed which outlined data formats
which should be adopted in the development of any BSN application. The following sections look at the different aspects of data processing such as the
use of on-node processing, the fusion of data, the use of context for autonomic sensing, and data mining techniques and trend analysis.
27.1 On-node Data Processing
This refers to algorithms that can be implemented directly on a sensor node, allowing the optimisation of resources in terms of energy and
communications. These techniques are also essential if abnormal events were to be detected on the sensor node, and alerts were to be generated at
that level.
27.2 Sensor Fusion
The use of multiple sensors with information fusion has the several main advantages compared to single sensor systems. These include improved
signal to noise ratios, enhanced robustness and reliability in the event of sensor failure, integration of independent features and prior knowledge,
reducing uncertainty and improved resolution, precision, confidence and hypothesis discrimination.
27.3 Context Aware and Autonomic Sensing
The contextual information is mainly focused on the user's activity, physiological status and the surrounding physical environment. Understanding the
context in which the user performs his/her activities is essential in comprehending the activities themselves and their relationship to prior and future
activities or events, as well as environmental changes. Context aware sensing and autonomic sensing are linked; the latter referring to networks that can
autonomically configure, optimise, manage, heal, protect, adapt, scale and integrate.
27.4 Data Mining and Trend Analysis
With large amounts of data obtained, efficient data-mining is essential to allow important patterns to be recognised, errors in the data highlighted and
trends to be noted. This section will cover approaches that have been successfully used to provide pattern recognition in Body Sensor Networks.
27.5 Falls Detection Algorithms
A variety of data analysis techniques incorporating value algorithms have been applied to the falls detection domain. Many of the approaches focus on
improving the selectivity of falls. The issue of false positives is the most significant issues limiting the reliability of body worn falls detectors. Many efforts
have focused on improving the classification base solely on the sensor by using a variety of mathematical techniques such as thresholding using
support data with sensor data, typically accelerometer based, to achieve greater selectivity. Alternatively, supporting data sources have been utilized to
improve selectivity such as the inclusion of sound or/and visual sensors to improve the accuracy of the falls detection.
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28 Standards
28.1 Advantages
Standards are a necessary part of the cycle of maturation of any emerging technology. Lets give a very simple example to illustrate why standards are
important.
Typically the cycle of technology development would start from a concept, idea or a market need (market pull). This would then perhaps be taken maybe
in an academic environment to a design-of-prototype stage. Once a prototype is available testing begins on the device and the initial user requirements
are analysed against actual performance. Probably after some tweaking and maybe even re-design (this can take a considerable length of time) the
prototype passes through this stage to a full blown beta version. Further stress testing may be perfromed (for medical applications, in accordance with
regulations) before it is decided that the device is capable of being commercialised. Once the device is commercialised, it can be sold to an end
customer.
However now the real problems begin ! If the device can not work with other devices in the area (unreliable, loss of data, latency, interference,
communications, privacy compromised etc) or if the device can not communicate with the 'outside world' (i.e. operate in a 'sandbox'), then the device is
effectively useless and as a result there will be no market demand. The technology will just sit and wait for the standards to catch up. In the case of
personalised healthcare syatems it is even more important than just a commercial loss to some trading company, the potential of wellness and improved
healthcare for indivuals will not be realised and there will be a major societal impact.
Standards are the means by which the proven/working/very cool technology can actually 'reach out' and proliferate. All the "How Do I...?", "What if..?"
questions will be answered in advance and the devices should operate seamlessly ideally in a 'plug and play manner'. The user doesnt have to be
technical or understand anything about the technology, the system just works. A great example is the USB standard where any device froma camera to
a phone to a printer can just plug in and 'work'.
From a manufacturer/supplier persepctive standards ensure that their product will 'fit' the market need, it will integrate easily and will not become
obsolete before its time. Because standards are usually share efforts between many organisations working to a common goal, the associated costs of
this work would be dramatically reduced (as opposed to going it alone), so it makes sense for organisations to band together as much as possible.
Therefore standards are necessary to encourage and accelerate adoption of new technologies and to enable a faster time to market.
In summary standards;
• Encourages value-added innovation
• Accelerates adoption of new technologies
• Catalyses industry growth and creates opportunity for many companies in the industry
• Faster time-to-market with improved features and price/performance.
28.2 Disadvantages
The main drawback to standard creation is the complexity (and tediousness) of the work. To specify standards to a very detailed level requires great
discipline, focus and expertise. Many organisations simply can not commit resources to the level of activity required and overhead required. It is also
often also leveled against standards that they can stifle innovation in that if there is 'one way to do something' then it doesnt encourage people to look
for smarter methods (the beaurocracy required to influence the change can put of people).
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29 Initiatives
29.1 Bluetooth SIG
Bluetooth (1) wireless technology is a short-range communications technology intended to replace the cables connecting portable and/or fixed devices
while maintaining high levels of security. The key features of Bluetooth technology are robustness, low power, and low cost. The Bluetooth specification
defines a uniform structure for a wide range of devices to connect and communicate with each other.
Bluetooth technology has achieved global acceptance such that any Bluetooth enabled device, almost everywhere in the world, can connect to other
Bluetooth enabled devices in proximity. Bluetooth enabled electronic devices connect and communicate wirelessly through short-range, ad hoc networks
known as piconets. Each device can simultaneously communicate with up to seven other devices within a single piconet. Each device can also belong
to several piconets simultaneously. Piconets are established dynamically and automatically as Bluetooth enabled devices enter and leave radio
proximity.
Bluetooth technology operates in the unlicensed industrial, scientific and medical (ISM) band at 2.4 to 2.485 GHz, using a spread spectrum, frequency
hopping, full-duplex signal at a nominal rate of 1600 hops/sec. The 2.4 GHz ISM band is available and unlicensed in most countries. More information
on Bluetooth is available at (1) or here
Bluetooth profiles have been defined for healthcare applications and they define how different applications use Bluetooth wireless technology to set up a
connection and exchange data. The Medical Devices Working Group of the Bluetooth SIG (ref) developed this profile to ensure that devices used in
medical, health and fitness applications can transfer data between devices in a secure and well defined way via Bluetooth wireless technology.
29.2 USB Personal Health Device Specification
In April 2007 the USB Implementers Forum (IF) announced (2) that it was a Personal Healthcare Devices Working Group. The groups initial goal is to
develope a USB based medical device specification. The group comprised of 14 major players in technology and healthcare will work to develope a
standardised device class for transporting personalised messages and data.
29.3 ISO/IEEE Standards for Personal Health
ISO/IEEE have published a set of standards (3) pertaining to personal health devices and telehealth. As recently as October 2008 new standards were
added. A list of these currently available is as follows;
• ISO/IEEE 11073-10404 - Pulse Oximeter
• ISO/IEEE 11073-10406 - Pulse / Heart Rate
• ISO/IEEE 11073-10407 - Blood Pressure
• ISO/IEEE 11073-10408 - Thermometer
• ISO/IEEE 11073-10415 - Weighing Scale
• ISO/IEEE 11073-10417 - Glucose
• ISO/IEEE 11073-10441 - Cardiovascular Fitness Monitor
• ISO/IEEE 11073-10442 - Strength Fitness Equipment
• ISO/IEEE 11073-10471 - Independent Living Activity
• ISO/IEEE 11073-10472 - Medication Monitor
• ISO/IEE 11073-20601 - Optimized Exchange Protocol
• ISO/IEE 11073-00101 - Guidelines for the Use of RF Wireless Technology
29.4 CEN
The European Committee for Standardization (CEN) is defining and revising a 5 part EHR standard: CEN/TC 251 - Health informatics: Reference
Model, Archetype Interchange Specification, Reference Archetypes, Security, Exchange Models.
29.5 Routing over Low-power and Lossy Networks (Roll)
The Internet Engineering Task Force IETF has begun a standards effort to provide one of the missing puzzle pieces for wireless sensor networks.
Aiming to define a spec for Internet Protocol by June 2009, the IETF's Routing over Low-power and Lossy Networks (Roll) group (4) is pursuing a
standard way for control and sensor nodes on Bluetooth, Wi-Fi and 802.15.4 nets to link to the broader Internet.The Roll effort which is led by Cisco will
assess requirements in sensor nets for use in home and industrial automation as well as in urban settings. It is building on the work of the IETF WPAN
group, which has specified use of IPv6 over low-power wireless nets. The current model of wireless sensor net deployments feature ad-hoc networks
with many translation gateways, leading to a complex and expensive architecture that doesn't scale. There are thousands and thousands of sensor
networks in place in cars and buildings today, but most do not use IP. The Roll initiative aims at standardising the communications around IP.
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29.6 Integrating the Healthcare Enterprise (IHE)
The IHE Initiative (5) is a long-term program sponsored by many professional societies to promote the coordinated use of healthcare IT standards and to
provide a common framework for multi-vendor systems integration. IHE uses existing standards such as DICOM and HL7 as the building blocks for
assembling larger integrated solutions, thus the IHE framework is not re-inventing a new standard in healthcare but provides a practical method to make
these standards work. IHE also stages "connectathons" and "interoperability showcases" in which many vendors assemble to demonstrate the
interoperability of their products. The IHE initiative acts at a higher level that the 'on the ground' standards bodies such as HL7 and DiCOM and works
with these bodies where they exist. The IHE remit is more of an end to end one i.e. ensuring total interoperability of a system end to end, and not just
one piece of it (as is the focus with most standards bodies). The IHE focus is primarily on medical imaging devices, radiology, cardiology, and Hospital
Information Technology (HIT) Systems.
29.7 Health Level 7 (HL7)
The Health Level 7 (HL7) organisation (6) was founded in 1987 as a not for profit organisation to produce a standard for hospital information systems
exchange. HL7, Inc. is a standards organization that is accredited by the American National Standards Institute (ANSI); it became accredited in 1994. It
is an international community of healthcare subject matter experts and information scientists collaborating to create standards for the exchange,
management and integration of electronic healthcare information. HL7 is now adopted by ISO as a centre of gravity in international standardization and
accredited as a partnering organization for mutual issuing of standards. The name "Health Level-7" is a reference to the seventh "application" layer of
the ISO OSI Model. The name indicates that HL7 focuses on application layer protocols for the health care domain, independent of lower layers. HL7
effectively considers all lower layers merely as tools.
The basic idea of HL7 is to provide a common messaging format for health information systems to communicate with each other i.e. to provide a
'language' that all systems understand. EDI in the retail is a simple messaging system for the interchange of trading information such as Advanced
Shipping Notice and Order Transactions. HL7 aims at a area it defines very structured and semantic messaging schemes. However unlike EDI, HL7 is
very detailed and semantically structured. Typically healthcare information systems will be incompatible and not communicate with each other as very
often they have 'grown up' in the organisation seperately. So when the situation exists where data is needed to be exchanged, the different systems
esentially speak a different language. HL7 is designed to define a common language and enable disparate systems to communicate effectively. HL7 will
be very important for the proliferation of Electronic Medical Records if a truly nationwide solution (and even trans-national solution) is to be realised. HL7
will be very important for wireless sensor networks also, as when the amount of intelligent devices producing healthcare information grows as it will, a
common framework will be needed to maximise the promise of these networks. As with an emerging technology, there are many different approaches in
many academic/research settings at present that are essentially trying to do the same thing. HL7 will be an important component in developing a
common standard system for interoperability. For example, there is very little point in having a sensor network system that can not communicate its data
with physicians, caregivers, medical records etc in a secure and reliable manner.
An example HL7 Message is shown here.
29.8 Continua - Promoting Personal Health Systems Interoperability
The Continua Alliance (7) is a group of leaders from the medical device, technology and healthcare provider indistries that have come together to
promote a set of interoperable standards specifically for personal health or 'telehealth'. The Alliance which numbers over 176 members including many
blue chip technology companies and leading healthcare providers, looks at design guidelines that will enable vendors to build interoperable sensors,
home networks, telehealth platforms, and health and wellness services. They are also working on providing a 'certification' process for products that
conform to the continua standards and such products will carry the Continua logo. They also hava a brief of working with government agancies and
healthcareproviders to proliferate lower cost telehealth solutions.
Continua aims to enable alignment of different vendors and domains.
• Disease management - Chronic disease management
• Aging independently - Using technology and services to live in your home longer
• Health and Fitness - Expanding personal health and wellness
The Continua Alliance has selected what they call the 'Version One' connectivity standards (see IEEE Device Connectivity standards section) and is
working to identify and resolve gaps in some standards bodies so that personal telehealth solutions are interoperable and contribute toward improved
health management. Additionally, the Alliance is writing guidelines on specifically how to use the standards to achieve true interoperability across many
companies and many devices.
29.9 References
• 1. http://www.bluetooth.com/bluetooth/
• 2. http://www.usb.org/press/press20/2007_04_03_usbif.pdf
• 3. http://www.itu.int/itudoc/itu-t/workshop/e-health/addinfo/info009.html
• 4. http://www.ietf.org/html.charters/roll-charter.html
• 5. http://www.ihe.net
• 6. http://www.hl7.org
• 7. http://www.continuaalliance.org/
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30 Connectivity
Body sensor networks and wireless sensor networks in general will only fulfil their promise when the loop from the individual to the knowledge centre
(physician, monitoring centre etc) and back again to the individual is closed. This requires that the the sensed physical data can be reliably and securely
and with the individuals privacy respected, sent to a knowledge centre, it can then be actioned back to the individual (if action is needed). This is a
closed loop system now and promises tremendous advantages in terms of wellbeing if it can be achieved. Key component of this loop are the
communications mechanisms both on-body communications and body to back-end systems communications. As an example, there is little point in
having a very good and accurate sensor system monitoring say on-body ECG if the data that arrives at the physician or monoitoring centre is 'old' i.e. if
the communications channels are either too slow or congested - the individual may have had a heart attack before the warning data even arrives! Of
course the ideal scenario also is that this loop is closed in as near to real time as possible and this will be a goal of sensor networks as we move
towards the so called 'Internet of Things'
30.1 Technologies for Connectivity
The success of the body sensor network will only be as good as the communications mechanism that is adopted, both the on body system and the
person to monitoring centre/system link. Unreliable communications, packet loss, latency etc all detract from a successful system and should be
avoided. The Connectivity aspect therefore for assisted living and body sensor network technologies can be divied into catagories:
30.2 Inter-Network Connectivity
These can be wired or wireless connections between sensors, devices and aggregrators providing local connectivity within the home environment or the
personal area network. An example would be a body sensor network with two or three sensor nodes monitoring say ECG, respiration and Pulse
oxidation and sending the data to an on-body control unit. This sensor to sensor and sensor to control unit communication can take various forms
including
30.2.1 Bluetooth
Bluetooth wireless technology (1) is a short-range communications technology intended to replace the cables connecting portable and/or fixed devices
while maintaining high levels of security. The key features of Bluetooth technology are robustness, low power, and low cost. The Bluetooth specification
defines a uniform structure for a wide range of devices to connect and communicate with each other.
Bluetooth technology has achieved global acceptance such that any Bluetooth enabled device, almost everywhere in the world, can connect to other
Bluetooth enabled devices in proximity. Bluetooth enabled electronic devices connect and communicate wirelessly through short-range, ad hoc networks
known as piconets. Each device can simultaneously communicate with up to seven other devices within a single piconet. Each device can also belong
to several piconets simultaneously. Piconets are established dynamically and automatically as Bluetooth enabled devices enter and leave radio
proximity.
Bluetooth technology operates in the unlicensed industrial, scientific and medical (ISM) band at 2.4 to 2.485 GHz, using a spread spectrum, frequency
hopping, full-duplex signal at a nominal rate of 1600 hops/sec. The 2.4 GHz ISM band is available and unlicensed in most countries. More information
on Bluetooth is available at (1) or here
30.2.2 802.15.4 (Zigbee)
ZigBee (2) technology is a low data rate, low power consumption, low cost, wireless networking protocol targeted towards automation and remote
control applications. IEEE 802.15.4 committee started working on a low data rate standard a short while later. Then the ZigBee Alliance and the IEEE
decided to join forces and ZigBee is the commercial name for this technology. There is a lot of information on 802.15.4 available at (2) or via the
Wikipedia entry Zigbee.
30.2.3 Bluetooth vs Zigbee
• ZigBee looks rather like Bluetooth but is simpler, has a lower data rate and spends most of its time snoozing. This characteristic means that a
node on a ZigBee network should be able to run for six months to two years on just two AA batteries.
• The operational range of ZigBee is 10-75m compared to 10m for Bluetooth(without a power amplifier).
• ZigBee sits below Bluetooth in terms of data rate. The data rate of ZigBee is 250kbps at 2.4GHz, 40kbps at 915MHz and 20kbps at 868MHz
whereas that of Bluetooth is 1Mbps.
• ZigBee uses a basic master-slave configuration suited to static star networks of many infrequently used devices that talk via small data
packets. It allows up to 254 nodes. * Bluetooth?s protocol is more complex since it is geared towards handling voice, images and file transfers
in ad hoc networks.
• Bluetooth devices can support scatternets of multiple smaller non-synchronized networks(piconets). It only allows up to 8 slave nodes in a
basic master-slave piconet set-up.
• When ZigBee node is powered down, it can wake up and get a packet in around 15 msec whereas a Bluetooth device would take around 3sec
to wake up and respond.
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30.2.4 IEEE 802.11 (WiFi)
IEEE 802.11 is an evolving family of specifications for wireless local area networks (WLANs) developed by a working group of the Institute of Electrical
and Electronics Engineers (IEEE). There are several specifications in the family and new ones are occasionally added. All the 802.11 specifications use
the Ethernet protocol and Carrier Sense Multiple Access with collision avoidance (CSMA/CA) for path sharing. The original modulation used in 802.11
was phase-shift keying phase shift keying (PSK). However, other schemes, such as complementary code keying (CCK), are used in some of the newer
specifications. The newer modulation methods provide higher data speed and reduced vulnerability to interference. There many versions of 802.11
including the .a, .b, .g and .n versions with the variances mainly referring to data rate and througput differences. There is much information on 802.11
available here (3).
30.2.5 Ultra Wide Band (UWB)
UWB is defined as any radio technology having a spectrum that occupies a bandwidth greater than 20 percent of the center frequency, or a bandwidth
of at least 500 MHz (hence th eterm Ultra-Wide). Ultra Wide Band communications allows for high data throughput with low power consumption for
distances of less than 10 meters, or about 30 feet, which is very applicable to digital home requirements. It is touted as the next big thing for personal
area networking where many devices are involved, low power is a must and high data rates are important (medical monitoring). It operates at 3-10GHz
for medical applications and data rates of between 850 kbps to 20 Mbps. Since UWB operates at very high frequencies it has very high penetration loss
which will significantly affect the performance and size of the implantable nodes in a Body sensor network application. Thus any wide body area network
standard will most likely incorporate a narrow band together with the UWB technology to cover many environments in future. The standard is still
accepting proposals for the development of the body area networks, the UWB wireless chips are not available commercially to apply WBAN at the
moment. Although UWB was claimed very low power initially in the literature, the attempts of such technology in the integrated circuits have exhibited
power consumption more than that of the conventional narrowband short range wireless chips. UWB technology is very promising for real time locaation
systems though (RTLS) where accurate location whereabouts is important. Much work is being done in this area with the use of RFID devices for
medical asset tracking within hospitals.
A msjor drawback to date with UWB has been the standards issue. In January 2006 the Institute of Electrical and Electronic Engineers (IEE) abandoned
its efforts for standardisation or the 802.15.3a Task Group (TG3a). The two groups developing ultrawideband (UWB) technology failed to come to
agreement on a single solution: As a result, the IEEE group responsible for finalizing the standard didn?t get the votes required to move onto the next
stage of the standardization process. The IEEE task group TG3a reduced the number of competing standards from 23 to two: MultiBand Orthogonal
Frequency Division Multiplexing UWB, supported by the WiMedia Alliance and including members Intel and Microsoft, and the UWB forum which
favours Direct Sequence-UWB and includes members Motorola, Samsung and Sony. Unfortunately, the two technologies differ significantly and cannot
interoperate and so a classic standards dilemma exists.
30.2.6 Comparison of the Technologies
A good visual comparison of the internetwroking technologies is given here.
30.3 Backhaul Network Access Technologies
These can be wired or wireless connections providing connectivity with remote services, central processing units or data aggregration facilitities.
Examples would include body sensor networks connected back to healthcare information servers located in a hospital monitoring centre. Often this link
is referred to as 'backhaul' and it is the data pipe that brings the sensed healthcare data back to a centre where it can be actioned. Although narrowband
solutions (such as 56k modem type) can be used if the data rates are low and network latency is permissible, the key breakthrough here is the
availability of Broadband technologies such as ADSL, WiMAX, 3G and satellite communications.
30.3.1 Broadband Proliferation
Since December 2004, broadband subscribers in the OECD have increased by 187%, reaching 221 million in June 2007 and 380 million in September
2008. Broadband is available to the majority of inhabitants even within the largest OECD countries. A number of countries have reached 100% coverage
with at least one wired broadband technology and up to 60% with coverage by two.
30.3.2 Infrastructure
• Digital Subscriber Line (DSL) Access - Digital Subscriber Line access is a popular method of delivering broadband connectivity due to the fact
that it is transmitted over the standard telephone lines (POTs). Thi sfact ensures that it has wide coverage and deployment to the home is
relatively inexpensive. The common DSL technology is called ADSL i.e. Asymmetric Digital Subscriber Line so called because the data rate
downstresm (uploading from the internet) is higher than the upstream (pushing data out to the internet) data rate. Data rates can vary
depending on the version of DSL being deployed, however typical downstream rates cna be aorund 8Mbits/sec with upstream rates around 1
Mbits/sec. A DSL line can carry both data and voice signals and the data part of the line is continuously connected i.e 'Always On'. In the
OECD area, DSL networks have the most extensive broadband coverage overall. DSL coverage is particularly high in Belgium, Korea,
Luxembourg, the Netherlands and the United Kingdom. In 2005, 22 OECD countries had at least 90% coverage measured by lines,
households or population. Greece had the lowest DSL coverage in the OECD area with only 9% of the population able to obtain a DSL line in
2005.
• Cable Access- Cable providers have made impressive gains upgrading networks and offering broadband services to the majority of homes
previously without cable television. Broadband coverage by cable networks is very high in countries such as the United States, Canada,
Korea, Belgium and the Netherlands. In some areas it is even more extensive than DSL. A good source of information on Cable access is
located here
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• Fibre to The Home Networks (FTTH) - These networks bring high speed optical fibre links directly into the home where traditionally copper
based POTs or Cable would reside. The obvious advantage is high speed and high data rates.Fiber optic cables are made of glass fiber that
can carry data at speeds exceeding 2.5 gigabits per second (gbps). FTTH services commonly offer a fleet of plans with differing speeds that
are price dependent. At the lower end of the scale, a service plan might offer speeds of 10 megabits per second (mbps), while typical ADSL
service running on existing copper lines is 1.5 mbps. A more expensive FTTH plan might offer data transfer speeds of over 100 mbps (that's
about 66 times faster than typical DSL). Many of these networks have been in metropolitan areas since the density reduces infrastructure
costs on a per subscriber basis. The cities of Amsterdam, Vienna, Reykjavik and Paris all have FTTH networks in the planning or rollout
stages. NTT of Japan has the largest FTTH network rollout in the world in terms of total homes connected. Verizon in the United States is
upgrading users to fibre connections and they plan on passing 9 million homes with fibre by year-end 2008 and 18-20 million homes by 2010
• WiMAX (Worldwide Interoperability for Microwave Access) - This wireless broadband technology provides up to 75 Mb/s symmetric
broadband speed without the need for cables. The technology is based on the IEEE 802.16 standard (also called Broadband Wireless
Access). The name "WiMAX" was created by the WiMAX Forum, which was formed in June 2001 to promote conformity and interoperability of
the standard. The forum describes WiMAX as "a standards-based technology enabling the delivery of last mile wireless broadband access as
an alternative to cable and DSLFixed wireless access. WiMAX has become available in some rural areas but these networks serve only a
small percentage of subscribers. Wireless Internet access depends on available spectrum and OECD countries have taken steps to improve
the efficiency of spectrum use. The United States Federal Communications Commission (FCC) has been working to make a significant
amount of spectrum available for wireless broadband services. In September 2006, the FCC completed its auction of 90 megahertz of
Advanced Wireless Services spectrum. Then in January 2008, the FCC began auctioning an additional 62 megahertz of spectrum in the 700
MHz band, which is particularly well suited for wireless broadband
• 3G Access- 3G is the third generation of standards and technology for mobile networking, superseding 2.5G. 3G networks are wide-area (as
opposed to 802.11 or other Local Area Network technology) cellular telephone networks that evolved to incorporate high-speed Internet
access. Because of their reach, wireless Internet connections using 3G or emerging wireless networks will be an increasingly important but
largely complementary access technology to wired broadband. OECD countries already have extensive 2G coverage and many of these
networks are likely to be upgraded to 3G in the near future. All OECD countries have 2G mobile coverage of more than 90% of their
populations.11 Even large countries with extensive rural areas typically have excellent coverage of places where people live. Data shows that
subscribers are switching to 3G networks nearly as rapidly as they originally took up cellular/mobile phones. Third-generation mobile data
coverage is very high in a number of countries including Sweden, Korea, Luxembourg, Italy, the United Kingdom and the United States.
• Satellite Access- The broadband technology with the broadest geographic coverage is satellite. Geo-stationary satellites can supply
broadband over very large geographic areas. Early satellite broadband connections required a fixed-line return path (upstream data) but
current terminals can now transmit and receive data. Satellite has a large coverage area but only accounts for a small fraction of OECD
broadband connections ? largely due to its relatively high price compared with other connectivity options. Satellite connections are used for
backhaul and end-user connections in rural and remote areas and play a vital role connecting areas that have no other means of access.
Good technical detail on satellite access can be found here
• Broadband over Power Lines (BPL)- The least deployed technology involves the use of electrical power lines to transmit broadband.
Broadband over power lines (BPL), also known as power-line Internet or powerband, is the use of Power Line Communications technology to
provide broadband Internet access through ordinary power lines. A computer (or any other device) would need only to plug a BPL "modem"
into any outlet in an equipped building to have high-speed Internet access. International Broadband Electric Communications or IBEC and
other companies currently offer BPL service to several electric cooperatives. BPL may offer benefits over regular cable or DSL connections:
the extensive infrastructure already available appears to allow people in remote locations to access the Internet with relatively little equipment
investment by the utility. Also, such ubiquitous availability would make it much easier for other electronics, such as televisions or sound
systems, to hook up. Unfortunately proliferation has been almost non existent. Denmark had 98 PLC subscribers at the end of 2006, while the
United States had just over 5,000 in June of the same year. BPL technology suffers from a number of issues. The primary one is that power
lines are inherently a very noisy environment. Every time a device turns on or off, it introduces a pop or click into the line. Energy-saving
devices often introduce noisy harmonics into the line. The system must be designed to deal with these natural signaling disruptions and work
around them. The second major issue is signal strength and operating frequency. The system is expected to use frequencies of 10 to 30 MHz,
which has been used for many decades by amateur radio operators, as well as international shortwave broadcasters and a variety of
communications systems (military, aeronautical, etc.). Power lines are unshielded and will act as antennas for the signals they carry, and have
the potential to interfere with shortwave radio communications. A recent judgement (4) against BPL from the US District of Columbia Circuit
Court looks set to retard significantly the further development of this technology, based on the excessive interference problem
30.3.3 Speed
Competition is a key to lowering prices but it also has a significant effect on the services and speeds available to businesses and consumers.
Broadband quality tends to increase over time even as prices decline. This is a common feature in the ICT sector but broadband changes have been
particularly rapid. At the end of 2004 the average DSL speed across the OECD was less than 2 Mbit/s. The average advertised broadband speed had
more than quadrupled to nearly 9 Mbit/s over a period of less than three years. The average speed of advertised connections increased from 2 Mbit/s in
2004 to almost 9 Mbit/s in 2007. However the actual speed delivered to the customer can vary greatly form the 'advertised' speed and this has been an
issue of some contention and has led to a lack of trust by consumers. A report from the United Kingdom regulator OFCOM found that only 20% of
customers live close enough to a telephone exchange (3.2 kilometres) to receive the advertised 8 Mbit/s internet connection. If a user lives even further
from the exchange, say 8 kilometres away, users may only receive between 0.5 and 2 Mbit/s. There is also a significant difference between connection
speeds between rural and urban areas. The European Commission finds that download speeds between 144 kbit/s and 512 kbit/s have been the most
common in rural areas in the past two years. In contrast, the most common speeds in urban areas are closer to 1 Mbit/s. The fastest advertised
broadband connections offered by incumbent telecommunication operators were (2007) in Japan, Korea, Sweden, France and Finland. NTT in Japan
offers 1 Gbit/s connections to apartment buildings while the other operators offer Fibre to the home (FTTH) at 100 Mbit/s to individual apartments or
houses.
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30.3.4 Cost
Price Per Mbit per Second. Source: Broadband Growth and Policies in OECD Countries ? OECD 2008
Prices have an impact in areas with wired coverage as well and can be a strong determinant of broadband take-up. According to a recent OECD report
(5), between 2005 and 2006 the average price for a DSL connection fell by 19% and by 16% for cable Internet connections. Broadband is also
affordable in most OECD countries. The price of a broadband subscription in 20 of the 30 OECD countries was less than 2% of monthly GDP per capita
in October 2007. The introduction of entry-level broadband plans has helped broadband operators increase the number of total subscribers while still
offering the possibility for customers to pay more for faster speeds. Unbundling of copper telephone lines itself seems to be a factor in reducing the price
of broadband subscriptions, as they introduce more competition at the telecommunication exchange.
30.3.5 Coverage
Broadband Coverage - Source: Broadband Growth and Policies in OECD Cuntries ? © OECD 2008
Coverage statistics and penetration rate data show that operators and governments have made great strides extending broadband to rural and remote
areas. Satellite services are available in even the most remote areas of many OECD countries, although these tend to be more expensive relative to
other access technologies. Many governments have also implemented broadband demand aggregation policies to bring connectivity to rural areas.
High-speed wireless/mobile Internet connections are increasingly available as an important option for users.
30.4 Government Policy
In February 2004, the OECD Council adopted the Recommendation of the Council on Broadband Development. The Recommendation calls on Member
countries to implement a set of policy principles to assist the expansion of broadband markets, promote efficient and innovative supply arrangements,
and encourage effective use of broadband services. The Council instructed the OECD Committee for Information, Computer and Communications
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Policy (ICCP) to monitor the development of broadband in the context of this Recommendation within three years of its adoption and regularly thereafter.
Promoting the general ICT business and policy environment, fostering innovation in ICT (including R&D) as well as ICT diffusion and use (including
e-government) have been priorities. Likewise, ICT skills and employment, digital content and promoting trust have been key concerns. In particular,
OECD governments have implemented demand-based approaches for spreading broadband access. Policy makers have made particular efforts
connecting schools, libraries and other public institutions. Overall, these policies have led to increased use of broadband across the board.
Governments have also fostered broadband content and applications, for example, by acting as model users, by promoting e-government services and
broadband-related standards, by putting content online and by supporting the development and distribution of digital content by other players.
Governments and industry have also put into place regulatory measures to promote a culture of security. On the consumer protection side there has
been focus on developing awareness campaigns to educate consumers about risks to Internet security; they have also instructed consumers on how to
protect themselves against fraudulent practices.
30.5 References
• 1. http://www.bluetooth.org
• 2. http://www.zigbee.org
• 3. http://standards.ieee.org/getieee802/802.11.html
• 4. http://news.cnet.com/8301-10784_3-9930223-7.html
• 5. BROADBAND GROWTH AND POLICIES IN OECD COUNTRIES ? ISBN-978-92-64-04668-9 © OECD 2008.
http://www.oecd.org/dataoecd/32/57/40629067.pdf
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31 Digital Health Records
31.1 Introduction
Electronic Health Records and Electronic Medical Records allow for digital recording of health information for use by patients or medical professionals.
Electronic health records present an opportunity for cost savings and streamlining of administration, particularly in a telehealth scenario. Electronic
Health records may be generated and stored by medical or healthcare professionals, or by the user/patient (referred to as 'Personal Health Records').
As EHRs are not yet in universal or even general use there are two 'classes' of existant data: EHR information that has been scanned in or generated at
a later date than from the original hard-copy or paper record and EHR information that has existed in digital form from the start. Paper records may
suffer from illegibility, degradation, incompatibility with current formats.
31.2 Electronic Medical Records (EMRs) and Electronic Health Records (EHRs)
The two terms Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) are often used interchangeably. In fact they are not quiet the
same thing and it is important to establish the differences.
Electronic Medical Record - EMR: Definition according to Himmsanalytics (1). An application environment composed of the clinical data repository,
clinical decision support, controlled medical vocabulary, order entry, computerized provider order entry (CPOE), pharmacy, and clinical documentation
applications. This environment supports the patient s electronic medical record across inpatient and outpatient environments, and is used by healthcare
practitioners to document, monitor, and manage health care delivery within a care delivery organization (CDO) i.e. hospital, clinic or other care facility.
The data in the EMR is the legal record of what happened to the patient during their encounter at the CDO and is owned by the CDO.
Electronic Health Record - Is owned by the patient and has patient input and access that spans episodes of care across multiple CDOs within a
community, region, or state or countries. The EHR can be established only if the electronic medical records of the various CDOs have evolved to a level
that can create and support a robust exchange of information between stakeholders within a community or region.
So an electronic health record (EHR) is the super set of all Electronic medical recdords (EMRs). Where as the EMR may report only part of the overall
health picture and will be concerned with treatment and care visit details, the EHR will be the overall view on all the EMR entries. For example a person
receiving care in different CDOs, each with their own EMR and that do not share data with each other. The EHR will span these systems and collate the
data in to an overall record that the patient owns and cann add details (for example personal health data, fitness data, weight, etc). A good example of a
recent EHR is Google Health (2).
There is a nice illuatration here of the structure of EMRs and EHRs and the boundaries of each.
The Center for Information Technology Leadership has described four different categories or levels at which information exchange may take place:
• Non-electronic data - Paper, mail, and phone call.
• Machine transportable data - Fax, email, and unindexed documents.
• Machine organizable data (structured messages, unstructured content) - HL7 messages and indexed (labeled) documents, images, and
objects.
• Machine interpretable data (structured messages, standardized content) - Automated transfer from an external lab of coded results into a
provider's EHR. Data can be transmitted (or accessed without transmission) by HIT systems without need for further semantic interpretation or
translation.
(TABLE taken from Wikipedia)
31.3 Components of an Electronic Medical Record
• Administrative System Components - Registration, admissions, discharge, and transfer (RADT) data are key components of EHRs. These
data include vital information for accurate patient identification and assessment, including, but not necessarily limited to, name, demographics,
next of kin, employer information, chief complaint, patient disposition, etc
• Laboratory Systems - Laboratory systems generally are standalone systems that are interfaced to EMRs. Typically, there are laboratory
information systems (LIS) that are used as hubs to integrate orders, results from laboratory instruments, schedules, billing, and other
administrative information.
• Radiology Systems - Radiology information systems (RIS) are used by radiology departments to tie together patient radiology data (e.g.,
orders, interpretations, patient identification information) and images. The typical RIS will include patient tracking, scheduling, results
reporting, and image tracking functions. RIS systems are usually used in conjunction with picture archiving communications. The market for
PACs systems is quite mature in fact however very few systems still tie in with an EMR.
• Pharmacy System - Pharmacies in healtchare settings can range from totally manual paper based systems requiring much human
involvement, to highly automated and electronic systems with sophisticated put away, picking, packing and verification processes. One aspect
though is common generally - they pharmacies operate in a silo mode i.e standalone systems that do not communicate with other systems.
• Computerized Physician Order Entry (CPOE) - CPOE systems enable physicians to order prescriptions, laboratory and radiology services
electronically i.e. by using a PC, Tablet PC, PDA or other input device. CPOE systems offer a range of functionality, from pharmacy ordering
capabilities alone to more sophisticated systems such as complete ancillary service ordering, alerting, customized order sets, and result
reporting. CPOE systems offer the potential of reducing medical errors (worng medications, wrong procedures etc). The disemmination rate of
CPOE though has been slow. CPOE systems are often viewed by doctors with some suspicion and it is often charged that these systems
actually slow down work (they can be seen as an unnecessary layer of extra beaurocracy).
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• Clinical Documentation - Clinical notes; patient assessments; and clinical reports, such as medication administration records. Examples
include; Physician, nurse, and other clinician notes, Flow sheets (vital signs, input and output, problem lists), Peri-operative notes, Discharge
summaries, Advance directives or living wills, Consents (procedural), Medical record/chart tracking, Staff credentialing/staff qualification and
appointments documentation
Ideal Characteristics of an EHR
• Data should be continuously updatable
• Data should be able to be anonymously used for quality assurance, epidemic monitoring/prediction, resource management
• Data should be able to be exchanged between different EHR systems (interoperability)
31.4 Issues
31.4.1 Implemenation
31.4.2 Interoperability
US: Office of the National Coordinator for Health Information Technology (ONC) -->Regional Health Information Organizations RHIOs
Certification Commission for Healthcare Information Technology (CCHIT) - develping standards and certification (US)
While there are yet no universally adopted standards for EHRs there are many existing standards in specific areas e.g. HL7, ISO TC 215
Enterprise Master Patient Index (EMPI) technologies are designed to help transfer of patient medical records between facilities. On admission to a
medical facility a patient may be assigned a Medical Record Number, which may not correspond to the same patients record number from another
facility.
Older Record Digitisation - older paper records should ideally be added into a users current EHR. This digitisation/scanning process is time
consuming and must be done to high standards to ensure all relevant data is captured.
31.4.3 Training
31.4.4 Security/Privacy
Systems must be in place to ensure no unauthorised access to an individuals EHR is possible. This includes access by outside parties (e.g. hackers,
identity thieves) as well as limiting access to sensitive infomation by authorised users (e.g. carers, billing departments, insurance companies). US: EHR
informations is referred to as Protected Health Information (PHI) and security and management are covered under the Health Insurance Portability and
Accountability Act (HIPAA). EU: several directives of the European Parliament and Council protect transfer and use of personal data, including that
contained in EHRs.
31.4.5 Technology Limitations
Portable, straighforward input devices (e.g. Tablet PCs, PDAs) only now becoming viable, systems capable of dealing with a huge amount of data with
many complex and potentially interacting variables.
31.4.6 Preservation
Storage of data and maintenance of equipment for reading specialised data (e.g. X-Rays, MRI, Ultrasound) or migration of that data into a currently
readable format.
31.4.7 Synchronisation of Data
31.4.8 Cost of implementation
Benefits may not be seen by small or local physicians yet they may bear costs of training/adjustme
31.4.9 Social and Organisational Resistance
Fear of big brother scenarios, resistance to standardisation from corporate sector (proprietary technologies)
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31.4.10 Liability Barriers
31.4.11 Customisation
31.4.12 Justification
Studies have shown cost savings and revenue gains from implementation of EHRs (CITE). Gains are also to be had in terms of easy and efficient
supply of patient relevant information to healthcare providers at appropriate times (e.g. on admission for emergency treatment, data mining for early
indicators of potential health problems). More studies needed to confirm this and indicate gaps.
31.5 Benefits
Improved Billing Accuracy, reduction of effort duplication, facilitation of clinical trials, improved access,
31.6 Research
Players
31.7 US
• Veterans Association-Computerized Patient Record System (CPRS) - VistA EMR
• National Institutes of Health (NIH) the umbrella organisation for sub-Institutes such as the National Library of Medicine or the National Institute
on Aging.
• Markle Foundation
31.8 EU
• UK - National Health Service (NHS)
• European Health Telematics Association
• European Health Telematics Observatory
31.9 Japan
• KLAS independent health IT monitoring and rating organisation
31.10 Projects
• Indivo - open source, web based personal health record system.
• The OpenEHR Foundation is a not-for-profit company dedicated to "improving healthcare in the information society" and "making the
interoperable, life-long electronic health record a reality" and are working on an interoperability roadmap.
31.11 Commercial Products
31.12 Personal Health Records
• Microsoft HealthVault
• Google Health
• No More Clipboard
• Health Trio
31.13 Electronic Medical Records
• Microsoft Amalga
• Google Health
• HealthConnect
• Epic Systems Corporation suppliers of health IT systems to large healthcare organisations.
• Cerner suppliers of healthcare information technology (HIT), in particular the Millennium suite of HIT solutions.
• GE Centricity is an extensive EMR system.
• The Eclipsys Sunrise EMR system
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31.14 Patient/User ID
• VeriChip - Applied Digital Solutions Inc
31.15 Players (links to VCs/Angels/Agencies/MNCs/SME)
• Microsoft Health Solutions Group
• Google Health
• Kaiser Permanente - currently the largest privately held EMR system in the world, branded "KP HealthConnect".
• Cerner suppliers of healthcare information technology (HIT), in particular the Millennium suite of HIT solutions.
• GE Centricity is an extensive EMR system.
31.16 Business Models
Most current business models based around DHRs are either in the supply of systems to organisations or individuals and the provision of health record
data collection and records maintenance e.g. MediConnect Global-for a fee, they will gather a user's medical records from around the world and add
them to his or her GoogleHealth profile or Passport MD who provide a similar service.
31.17 Standards
31.18 HL7
• ISO: ISO 13606-2:2008 Health informatics -- Electronic health record communication -- Part 2: Archetype interchange specification
• HIMSS Electronic Health Record Association is a US trade association of vendors of EHRS, working towards standards for interoperability
and standards development and certification.
• European Institute for Health Records
• The European Committee for Standardization (CEN) is defining and revising a 5 part EHR standard: CEN/TC 251 - Health informatics:
Reference Model, Archetype Interchange Specification, Reference Archetypes, Security, Exchange Models.
31.19 Gaps
31.20 Gaps in technology
31.21 Gaps in the basic science
31.22 Gaps in operation
31.23 Gaps in implementation
31.24 Future Vision
31.25 References
1. http://www.himssanalytics.org/docs/WP_EMR_EHR.pdf 2. https://www.google.com/health
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32 User Centered Design for Independent Living
32.1 Introduction
As indicated by C. Magnusson in ?Enaction and Enactive Interfaces: a Handbook of Terms?, User Centered Design can be defined as ?an approach to
guarantee the usability of interactive systems, by actively involving the end-user?. This means that the main focus in User Centered Design is on
interaction and on end-users? needs and skills. In the case of Ambient Assisted Living (AAL), systems should be designed for different classes of
end-users having different needs and skills. Such different classes include:
• Elders or impaired people, needing e.g., for support for therapy, support in daily activities, self-monitoring, systems helping them in
overcoming specific impairments, flexible systems adapting to user?s functional limitations; it is particularly important that systems can be
transparent to the user;
• Caregivers, needing e.g., systems for daily monitoring of physiological signals, including alarms in case of possible dangers;
• Clinicians, needing e.g., systems for periodical monitoring of specific medical data supporting evaluation of advancement of the pathological
conditions and response to therapy; filtering of large amounts of data in order to focus attention on relevant aspects;
These requirements can be satisfied by a system whose interfaces adapt to the different kind of end-users. In Human Computer Interaction HCI the user
interface is the part of the program that is directly in contact with humans, i.e., it is the part of the application which is most responsible of usability and
interaction. The design of the interfaces plays a key role in the access to technology and in its comprehension. In AAL interfaces have to be particularly
simple, adaptable, intuitive as defined by Baerentsen: ?An intuitive interface may be defined as an interface, which is immediately understandable to all
users, without the need neither for special knowledge by the user not the initiation of special educational measures?. To this aim, interfaces can exploit
several communication channels and human mechanisms of non-verbal communication. In other words, AAL systems need adaptable and intuitive
multimodal interfaces.
Usability in AAL has a crucial role and for the seniors it is strongly related to their functional limitations. The experience of interaction with a computer
can be compromised, for example, by reduced cognitive and /or motoric capabilities as a consequence of diseases affecting elder people. On the one
hand, a bad design of an application can hurt the elder user until to inducting computer anxiety, rejection of the technology, perception of isolation with
respect to the society, and depression. On the other hand, a good design of the interface can encourage the elder in technology usage highlighting the
benefices as to improve mental and physical wellbeing and to enhance the social connection and consciousness. Furthermore, a good design can also
compensate some of the functional limitations, for example by means of multimodal interaction i.e., analyzing the multimodal communication of humans
(e.g voice and gestures) and producing multimodal feedback (e.g. acoustic and visual).
32.2 Issue
There are many aspects to consider in the design of an interface for independent living, from limitations in cognitive, motoric and audio-visual abilities of
elder people, to the interaction with different categories of subjects (seniors, clinicians and so on). In particular a user centered interface for AAL needs:
• novel multimodal interfaces and novel behavior descriptors to monitor more carefully elder patients;
• novel approaches for rehabilitation exercises based on « aesthetical resonance » paradigms. The aim is to develop interactive therapeutic
exercises based on multimodal interaction and interactive multimedia (audiovisual) stimulation in real-time;
• a better support, especially for clinicians, by automated quantitative and qualitative measures of the evolution over time of the therapy and/or
the performance of motor tasks;
• novel paradigms of interaction that allow to simplify the interaction with home systems and devices and/or to support social interaction, e.g.,
with new possibilities to exchange experiences or enjoy the time (see for details Social_Connectedness).
32.3 Functional limitations
The design of interfaces for elder users should consider the well known limitations of motoric response and the sensory-perceptual process. For
example, a correct design of the position and size of icons can improve performance in the reaching problem, a common problem also for children.
There are a number of studies on this topic, here we report just an overview on some functional limitations. It is important to highlight that at the time
being elders have not the same expertise in using technologies than younger people, and that in some cases they do not have any experience at all.
Functional limitations that need to be taken into account in designing interfaces include:
1. Visual ability. Excluding singular ocular pathology, elder people shows a reduction of dynamic visual acuity, a reduction in the range of visual
accommodation and, finally, reduction in colour sensitivity. Usually, there is a loss of contrast sensitivity and dark adaptation.
2. Auditory ability. In subject over 60 there is a decline in auditory acuity, i.e. in the sensitivity for pure tones and high frequency tones. There are
problems in localizing sound and binocular hearing.
3. Motoric impairments. Limitations in motoric skills include:
1. slower response;
2. reduction in ability to maintain continuous movement;
3. reduction in coordination and in balance (usually, this is a consequence of the before-mentioned limitations);
4. loss of flexibility.
4. Cognitive process decline, e.g. limitation of the attention or difficult in remembering information ( for examples see Dementia )?
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32.4 Research
The research effort on centered design for independent living up to now is mainly devoted to create solution for remote monitoring of seniors at home or
to allow self monitoring. For a generalized overview on the research activities there is the Institute for Human Centre Design that try to support the
community and disseminate results. A good example of software platform that address the requirements of flexibility, adaptability, and multimodality of a
system and its interface is the EyesWeb Mobile application developed by InfoMus Lab ? DIST - University of Genova.
A parallel approach is related to the design of multi-functional Robotic systems, able to supply daily assistance on health-support and daily life activities
(DLA). These Robotic systems are design to interact with humans using high level information, that means such Robots can sense Kansei factors from
humans and try to use the same communication channel for communicating with humans (Robotics).
32.4.1 EyesWeb XMI Server and EywRAD client [1]
The EyesWeb XMI server exploits the EyesWeb XMI open platform and the EyesWeb Expressive Gesture Processing Library to provide services
related to multimodal, including physiological, signals. EyesWeb XMI manages the real-time synchronization of multimodal streams of data having
different clocks. The EyesWeb XMI server can thus analyze simultaneously, and in a transparent way for the user, signals from a wide range of devices
(e.g., video cameras, microphones, physiological sensors, shock sensors, accelerometers). As a result from such analysis, the EyesWeb XMI server
can provide in real time metadata related to embodiment, expressivity, and gesture. The EyesWeb Mobile client is an application for both desktop
computers and mobile devices running Windows Mobile operating system. In its current form, it is a user interface for the remote control of EyesWeb
applications running on EyesWeb XMI servers. The EyesWeb Mobile client can support the transmission of the sensor inputs available on the mobile
device (e.g., webcam, audio input, accelerometers, gps, etc.) and can also exploit EyesWeb services to perform some processing of such data on the
mobile device itself (this may reduce the data to be transmitted, with benefit e.g. in battery duration in the mobile). The EyesWeb Mobile client comes
with a design and authoring tool that enables users to draw the user interface for a specific EyesWeb XMI patch. The designer tool enables the design
and implementation of user interfaces to control one or more EyesWeb XMI patches, using simple commands and widgets, and to have a simple
visualization of complex data or video streams. Using these technologies it is possible to access to an unique application in different way (i.e. using
Palm or monitor or mobile phones) and/or looking to different data set In this way for example a clinician can check all the medical parameters and the
subject can access to just to the subset he/she can understood.
32.4.2 Adaptive Environments [2]
Adaptive Environments, Institute for Human Centre Design, is an international non-profit organization, based in Boston. Its activities are mainly devoted
to promote design that works for everyone across the spectrum of ability and age and to enhance human experience. Adaptive Environments provides
easy access to information and guidance about the civil rights laws and codes that provide a bedrock of accessibility in the US. Adaptive Environments
provides education and consultation about strategies, precedents and best practices that go beyond legal requirements to design places, things,
communication and policy that integrate solutions to the reality of human diversity.
32.5 Commercial
The commercial solution are more oriented to the health monitoring or home care. Examples are:
http://www.instantatlas.com/health.xhtml commercial platform for monitoring and reporting of general health data. It is a general platform that works on
statistical data and maps, not only indoor. http://www.microsystems.it/index.php/ita/Azienda/Divisioni/Webcare Italian commercial solution for home
care. It uses a core station receiving the physiological data, a jacket with ECG sensors a blood pressure measurement wireless connected to the core
station. There are also possibilities for a mobile core station.
32.6 References
1. Enactive Network Partners,?Enaction and Enactive Interfaces: a Handbook of Terms" In A. Luciani and C. Cadoz (eds.), Enactive Systems
Books, Grenoble, 2007; ISBN 978-2-9530856-0-0
2. Baerentsen, K.B.,"Intuitive user interfaces" Scand. J. Inf. Syst. 12, 1-2 (Jan.2001), pp 29-60.
3. Wijnand Ijsselsteijn, Henk Herman Nap, Karolien Poels, Yvonne de Kort, ?Digital Game Design for Elderly Users? proc.of the 2007
Conference on Future Play, pp.17-22, ISBN 978-1-59593-943-2.
4. Czaja, S.J., & Lee, C.C. (2003). Designing computer systems for older adults. In J.A. Jacko & A. Sears (Eds.), The Human-Computer
Interaction Handbook ? Fundamentals, Evolving Technologies and Emerging Applications (pp. 425). Mahwah, New Jersey: Lawrence
Erlbaum Associates.
5. Fisk A.D., Rogers A.R., Charness N, Czaja S.J., & Sharit J. (2004). Designing for Older Adults ? Principles and Creative Human Factors
Approaches. Boca Raton: CRC Press.
6. Rogers, W., & Fisk, A. (2000). Human factors, applied cognition, and aging. In: F.I.M. Craik & T.A. Salthouse (Eds.), The Handbook of Aging
and Cognition. Mahwah, NJ: LEA
7. Melenhorst, A.-S. (2002). Adopting communication technology in later life. The decisive role of benefits. PhD Dissertation, Eindhoven
University of Technology
8. AAL
9. http://www.adaptiveenvironments.org.
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33 Privacy & Security
As sensor systems become more and more pervasive and truly start to operate in the background unobtrusively, issues of human privacy become a
major concern. Radio Frequency Identification (RFID) has been leading the charge in the deployment of 'intelligent' network nodes being widely
disseminated and many of the privacy issues brought about by RFID are common to body sensor network nodes. Many examples of body sensor
networks treat privacy through security mechanisms i.e. encryption and data protection throughout the hardware and software layers.
33.1 Fundamentals of Freedom
A popular dictionary defines privacy as: "The quality or condition of being secluded from the presence or view of others. The state of being free from
unsanctioned intrusion: a person's right to privacy". The commonly accepted definitions of Privacy that are built in to much legislation use the concepts
of a person?s right to be free from unreasonable search and seizure and intrusion. It also states that the protection of personal information is a
fundamental right. The United Nations Universal Declaration of Human Rights (1) which is exactly 50 years old, enunciates the fundamental right to
privacy and can be viewed at www.un.org/overview/rights.html . In summary it affirms;
• The right to dignity and freedom.
• People shall not be subjected to arbitrary interference with privacy and home.
• The right to freedom of movement and residence within the borders of the state.
• It also states that people shall have the right to freedom of thought conscience and religion and the right to practice and change if so decided
ones faith.
(Note: This last point may not seem relevant to a treatment of wireless sensors however the concern here is that many see technology as a barrier
between the physical and spiritual worlds as a slide away from traditional spiritual values into a materialistic irreligious society. People will refer to the
Book of Revelations and cite ?The Mark of the Beast?.)
33.2 OECD Guidelines on Privacy
The Organisation for Economic Cooperation and Development (OECD) released Guidelines for Data Protection and Privacy (2) in 1980 which was
based on the US initiated Fair Information Practises (FIPS) (3) policy. These Guidelines were reaffirmed in 1998 as still relevant and form the basis of
much legislation worldwide. Its key pillars are:
• 1. There must be no personal-data, record keeping systems whose very existence is a secret.
• 2. There must be a way for a person to find out what information about the person is in a record and how it is used.
• 3. There must be a way for a person to prevent information about the person that was obtained for one purpose being used for other purposes
without the persons consent.
• 4. There must be a way for the person to correct and amend a record of identifiable information about the person.
• 5. Any organization creating, maintaining, using or disseminating records of personally identifiable data must ensure the reliability of the data
for their intended use and must take reasonable precautions to prevent misuse of the data.
These five key principles form the basis of much privacy legislation world-wide and all wireless sensor network systems must at a minimum comply with
these guidelines.
The are major issues around privacy raised and ethics raised by the evolution towards a so called ubiquitous computing society and as the evolution
progresses, these issues become more important. In fact privacy/ethics will be a fundamental barrier to adoption unless handled proactively, as
presently technology has been outpacing policy.
33.3 Privacy Concerns of Wireless Sensor Networks
There are a number of recurring themes that constantly come up in the privacy arena regarding the proliferation of intelligent wireless devices such as
RFID and sensor networks. These are listed below. It is imperative that policy and legislation protect these areas and provide assurance to consumers.
However unfortunately one generally arrives back at the point of trust i.e. "how do I know that you are doing what you say, so I trust you..." and this is
the toughest challenge of all.
• Surveillance - The "Big Brother" scenario - Because wireless technology uses radio waves which are invisible to the human eye it is possible
to have devices implanted in areas that are hidden. Examples are beneath floors or in ceilings, behind walls etc. These readers can be
gathering individual data, unknown to the individual.
• Association - The concern is that a person could be associated with for example a product e.g. an ECG monitored patient could be associated
with a particular heart medication and be the victim of agressive marketing. Or an AIDs patient may suffer social exclusion based on the
medical condition.
• Profiling - Arises out of association, the concern here is that complete profiles of a person may be built up, i.e. likes, dislikes, health status,
political allegiances etc. A major concern is that this information could be used for subterfuge purposes i.e. blackmail.
• Data Sharing "One Big Database" - The question arises as to the boundaries of a wireless sensor network. Where does the data sharing end
and who owns the data once it passes multiple boundaries? Who has my information sitting on their database and how are they using it?
Despite assurances from the service providers, the consumer has the dilemma - how do I know that the data is not being shared with
agencies that I have not given consent to? The answer of comes down to 'trust' and this is one thing consumers very often dont have when it
comes to government or retail type organisations!
• Labour Impact ? Wireless sensor technologies are poised to transform manual processes such as found in healthcare environments and
provide a higher level of automation. This could imply the reduction of labour forces in certain areas or perhaps the redeployment of labour
forces to other areas. This will be a very sensitive topic, particularly where labour unions are strong as in parts of Europe.
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33.4 Not Everyone is Enthusiastic About this Technology
With the above concerns in mind, many groups have sprang up that lobby and campaign against the ubiquitous deployment of wireless technology. As
RFID systems have been ahead of commercial wireless sensor networks they have been in a way the lightning rod for a lot of potential privacy issues
that wireless sensor networks will encounter and the issues are almost identical (actually they will be tougher as we move to truly ubiquotous networks!).
A leading lobby group against this technology is called CASPIAN (Consumers Against Supermarket Privacy Invasion And Numbering) (4). They refer to
RFID devices as spychips and are concerned with personal information being used in an unauthorised manner. Quoting from their website
(spychips.com)... "We do believe, however, that these technologies pose serious risks to consumers, and we have called on the world's shoppers to
reject them. CASPIAN hopes to see both technologies (RFID and supermarket loyalty cards) ultimately fail in the marketplace as a result of consumer
opinion. In the long run, outright market failure would offer more effective consumer protections than temporary legislative band-aids. (What the
legislature grants, the legislature can easily take away, limiting the field of consumer espionage to itself."
This gives a flavour of some of the difficulties in the privacy debate and shows the need for it to be handled proactively and not 'bolted on' once the
technology is being deployed i.e. as an afterthought. The area of healthcare is one where the 'hearts and minds' debate can be easier to argue i.e.
people would be willing to trade off some privacy if their wellbeing or quality of life was improved. However for normally healthy people, the arguement
can be lost if for example as a result of remote monitoring, a persons home is broken in to and medication stolen or if information on a sensitive medical
condition is disseminated or if the person is the recipient of an agressive marketing campaign.
33.5 Global Policy and Legislative Efforts
33.6 European Union - Directive 95/46/EC (5)
33.6.1 Background
In May 2000, the Information Society Technologies Advisory Group (ISTAG) commissioned the creation of four scenarios (7) ?to provide food for
thought about longer term developments in Information and Communication Technologies?, with the intent of exploring the social and technical
implications of ambient intelligence. Among other things, the scenarios suggested a set of ?critical socio-political factors? that were considered crucial
for the development of ambient intelligence, including the issue of security and trust. ISTAG said that ?a key aspect is management of privacy: more
open systems tend to lower privacy levels [where] technological developments are outpacing regulatory adjustments
The ISTAG vision ?trust and confidence enabling tools for the management of privacy within an ambient intelligence context? became a major focus of
the ?Disappearing Computers? component of the EC?s Fifth Framework Programme (FP5) and provided a point of departure for structuring IST
research under the Sixth Framework Programme (FP6).
The governing policy in Europe regarding personal data is "Directive 95/46/EC on the protection of individuals with regard to the processing of personal
data and on the free movement of such data". The directive was implemented in 1995 by the European Commission. The right to privacy is a highly
developed area of law in Europe. All the member states of the European Union (EU) are also signatories of the European Convention on Human Rights
(ECHR). Article 8 of the ECHR provides a right to respect for one's "private and family life, his home and his correspondence," subject to certain
restrictions. The European Court of Human Rights has given this article a very broad interpretation in its jurisprudence. In 1981 the Convention for the
Protection of Individuals with regard to Automatic Processing of Personal Data was negotiated within the Council of Europe. This convention obliges the
signatories to enact legislation concerning the automatic processing of personal data, which many duly did.
33.6.2 Scope of Directive
Personal data are defined as "any information relating to an identified or identifiable natural person ("data subject"); an identifiable person is one who
can be identified, directly or indirectly, in particular by reference to an identification number or to one or more factors specific to his physical,
physiological, mental, economic, cultural or social identity;" (art. 2 a)
This definition is meant to be very broad. Data are "personal data" when someone is able to link the information to a person, even if the person holding
the data cannot make this link. Some examples of "personal data" are: address, credit card number, bank statements, criminal record, etc.
The notion processing means "any operation or set of operations which is performed upon personal data, whether or not by automatic means, such as
collection, recording, organization, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise
making available, alignment or combination, blocking, erasure or destruction;" (art. 2 b)
The responsibility for compliance rests on the shoulders of the "controller", meaning the natural or artificial person, public authority, agency or any other
body which alone or jointly with others determines the purposes and means of the processing of personal data; (art. 2 d)
The data protection rules are applicable not only when the controller is established within the EU, but whenever the controller uses equipment situated
within the EU in order to process data. (art. 4) Controllers from outside the EU, processing data in the EU, will have to follow data protection regulation.
In principle, any online business trading with EU citizens would process some personal data and would be using equipment in the EU to process the
data (i.e. the customer's computer). As a consequence, the website operator would have to comply with the European data protection rules. The
directive was written before the breakthrough of the Internet, and to date there is little jurisprudence on this subject.
33.6.3 Principles
Personal data should not be processed at all, except when certain conditions are met. These conditions fall into three categories: transparency,
legitimate purpose and proportionality.
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33.6.3.1 Transparency
The data subject has the right to be informed when his personal data are being processed. The controller must provide his name and address, the
purpose of processing, the recipients of the data and all other information required to ensure the processing is fair. (art. 10 and 11)
Data may be processed only under the following circumstances (art. 7):
• When the data subject has given his consent
• When the processing is necessary for the performance of or the entering into a contract
• When processing is necessary for compliance with a legal obligation
• When processing is necessary in order to protect the vital interests of the data subject
• Processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the
controller or in a third party to whom the data are disclosed
• Processing is necessary for the purposes of the legitimate interests pursued by the controller or by the third party or parties to whom the data
are disclosed, except where such interests are overridden by the interests for fundamental rights and freedoms of the data subject
• The data subject has the right to access all data processed about him. The data subject even has the right to demand the rectification,
deletion or blocking of data that is incomplete, inaccurate or isn't being processed in compliance with the data protection rules. (art. 12)
33.6.3.2 Legitimate purpose
Personal data can only be processed for specified explicit and legitimate purposes and may not be processed further in a way incompatible with those
purposes. (art. 6 b)
33.6.3.3 Proportionality
• Personal data may be processed only insofar as it is adequate, relevant and not excessive in relation to the purposes for which they are
collected and/or further processed. *The data must be accurate and, where necessary, kept up to date.
• Every reasonable step must be taken to ensure that data which are inaccurate or incomplete, having regard to the purposes for which they
were collected or for which they are further processed, are erased or rectified.
• The data shouldn't be kept in a form which permits identification of data subjects for longer than is necessary for the purposes for which the
data were collected or for which they are further processed.
• Member States shall lay down appropriate safeguards for personal data stored for longer periods for historical, statistical or scientific use. (art.
6)
• When sensitive personal data (e.g. religious beliefs, political opinions, health, sexual orientation, race, membership of past organisations) are
being processed, extra restrictions apply. (art. 8)
• The data subject may object at any time to the processing of personal data for the purpose of direct marketing. (art. 14)
• A decision which produces legal effects or significantly affects the data subject may not be based solely on automated processing of data. (art.
15) A form of appeal should be provided when automatic decision making processes are used.
Supervisory authority and the public register of processing operations Each member state must set up a supervisory authority, an independent body that
will monitor the data protection level in that member state, give advice to the government about administrative measures and regulations, and start legal
proceedings when data protection regulation has been violated. (art. 28) Individuals may lodge complaints about violations to the supervisory authority
or in a court of law.
The controller must notify the supervisory authority before he starts to process data. The notification contains at least the following information (art. 19):
the name and address of the controller and of his representative, if any; the purpose or purposes of the processing; a description of the category or
categories of data subject and of the data or categories of data relating to them; the recipients or categories of recipient to whom the data might be
disclosed; proposed transfers of data to third countries; a general description of the measures taken to ensure security of processing. This information is
kept in a public register.
33.6.4 Transfer of personal data to third countries
Third countries is the term used in EU legislation to designate countries outside the European Union. Personal data may only be transferred to third
countries if that country provides an adequate level of protection. Some exceptions to this rule are provided, for instance when the controller himself can
guarantee that the recipient will comply with the data protection rules.
The European Commission has set up the "Working party on the Protection of Individuals with regard to the Processing of Personal Data," commonly
known as the "Article 29 Working Party". The Working Party gives advice about the level of protection in the European Union and third countries.
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The Working Party negotiated with U.S. representatives about the protection of personal data, the International Safe Harbor Privacy Principles(11) were
the result. According to critics the Safe Harbor Principles do not provide for an adequate level of protection, because it contains less obligations for the
controller and allows the contractual waiver of certain rights.
33.7 Implementation by the member states
EU directives are addressed to the member states, and aren't legally binding for citizens in principle. The member states must transpose the directive
into internal law. Directive 95/46/EC on the protection of personal data had to be transposed by the end of 1998. All member states have enacted their
own data protection legislation
33.8 United States
There is currently no federal law applicable to the collection and processing of personally identifiable information gathered through the use of wireless
technologies. However many states are proposing legislation based on the FIPs guidelines. Examples include California, Virginia, Missouri and
Maryland. The Electronic Privacy Information Centre (EPIC) has reported (8) survey opinion data that indicate that Americans want total transparency
around how their information is used and collected and that consent must be obtained. They also do not trust self regulation and want the ability to view
the data that is retained at any time. They also state that many are extremely or very concerned about the privacy implications of RFID technologies.
The United States has no comprehensive privacy protection law for the private sector. A patchwork of federal laws covers some specific categories of
personal information (Privacy Act, COPPA, HIPAA, CAN-SPAM act PATRIOT act etc). These include financial records, health information, credit
reports, video rentals, cable television, children's (under age thirteen) online activities, educational records, motor vehicle registrations, and
telemarketing.
33.8.1 The Health Insurance Portability and Accountability Act (HIPAA)
Regarding medical information and data handling, the The Health Insurance Portability and Accountability Act (HIPAA) (9), applies in the United States.
The act is roughly broken in to two sections one of which protects health insurance coverage for workers and their families when they change or lose
their jobs. The second, known as the Administrative Simplification (AS) provisions, requires the establishment of national standards for electronic health
care transactions and national identifiers for providers, health insurance plans, and employers. The Administration Simplification provisions also address
the security and privacy of health data. The standards are meant to improve the efficiency and effectiveness of the nation's health care system by
encouraging the widespread use of electronic data interchange in the US health care system.
A good treatment of the HIPAA Act is given here Health_Insurance_Portability_and_Accountability_Act, and the full text of HIPAA is given in (9)
however some fey features include;
• The Privacy Rule - The Privacy Rule took effect on April 14, 2003. It establishes regulations for the use and disclosure of Protected Health
Information (PHI). PHI is any information about health status, provision of health care, or payment for health care that can be linked to an
individual.[10] This is interpreted rather broadly and includes any part of a patient?s medical record or payment history. ALL FIPs principles
apply including the individuals right to request information on his or her personal information being held, ensuring that the accuracy of this
maintained and the right to change personal details (such as contact numbers). Also included were provision for a complaints process in
instances where the individual believes the Privacy rules are not being honoured, a sort of Ombudsman process. However this has been
reported as being woefully inefficient and beaurocratic.
• The Security Rule - The Security Rule complements the Privacy Rule. While the Privacy Rule pertains to all Protected Health Information
(PHI) including paper and electronic, the Security Rule deals specifically with Electronic Protected Health Information (EPHI). It lays out three
types of security safeguards required for compliance: administrative, physical, and technical. For each of these types, the Rule identifies
various security standards, and for each standard, it names both required and addressable implementation specifications. Required
specifications must be adopted and administered as dictated by the Rule. Addressable specifications are more flexible. Individual covered
entities can evaluate their own situation and determine the best way to implement addressable specifications.
• The Unique Identifiers Rule (National Provider Identifier NPI) - Effective from May 2006, all covered entities using electronic communications
(e.g., physicians, hospitals. The NPI replaces all other identifiers used . The NPI is 10 digits (may be alphanumeric), with the last digit being a
checksum. The NPI cannot contain any embedded intelligence; in other words, the NPI is simply a number that does not itself have any
additional meaning. The NPI is unique and national, never re-used, and except for institutions, a provider usually can have only one. An
institution may obtain multiple NPIs for different "subparts" such as a free-standing cancer center or rehab facility.
• The Enforcement Rule - The Enforcement Rule sets civil money penalties for violating HIPAA rules and establishes procedures for
investigations and hearings for HIPAA violations.
33.8.2 Other Applicable Laws in the United States
For healthcare pruposes the main policy document is the HIPAA act, however some other acts also apply. An example is the Childrens On Line Privacy
Protection Act (COPA) (10). The act, effective April 21, 2000, applies to the online collection of personal information by persons or entities under U.S.
jurisdiction from children under 13 years of age. It details what a website operator must include in a privacy policy, when and how to seek verifiable
consent from a parent or guardian, and what responsibilities an operator has to protect children's privacy and safety online including restrictions on the
marketing to those under 13.
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33.9 Interoperability Between Health Information Systems - Health Level 7 (HL7)
The Health Level 7 (HL7) organisation (12) was founded in 1987 as a not for profit organisation to produce a standard for hospital information systems
exchange. HL7, Inc. is a standards organization that is accredited by the American National Standards Institute (ANSI); it became accredited in 1994. It
is an international community of healthcare subject matter experts and information scientists collaborating to create standards for the exchange,
management and integration of electronic healthcare information. HL7 is now adopted by ISO as a centre of gravity in international standardization and
accredited as a partnering organization for mutual issuing of standards. The name "Health Level-7" is a reference to the seventh "application" layer of
the ISO OSI Model. The name indicates that HL7 focuses on application layer protocols for the health care domain, independent of lower layers. HL7
effectively considers all lower layers merely as tools.
The basic idea of HL7 is to provide a common messaging format for health information systems to communicate with each other i.e. to provide a
'language' that all systems understand. EDI in the retail is a simple messaging system for the interchange of trading information such as Advanced
Shipping Notice and Order Transactions. HL7 aims at a area it defines very structured and semantic messaging schemes. However unlike EDI, HL7 is
very detailed and semantically structured. Typically healthcare information systems will be incompatible and not communicate with each other as very
often they have 'grown up' in the organisation seperately. So when the situation exists where data is needed to be exchanged, the different systems
esentially speak a different language. HL7 is designed to define a common language and enable disparate systems to communicate effectively. HL7 will
be very important for the proliferation of Electronic Medical Records if a truly nationwide solution (and even trans-national solution) is to be realised. HL7
will be very important for wireless sensor networks also, as when the amount of intelligent devices producing healthcare information grows as it will, a
common framework will be needed to maximise the promise of these networks. As with an emerging technology, there are many different approaches in
many academic/research settings at present that are essentially trying to do the same thing. HL7 will be an important component in developing a
common standard system for interoperability. For example, there is very little point in having a sensor network system that can not communicate its data
with physicians, caregivers, medical records etc in a secure and reliable manner.
An example HL7 Message is shown here.
33.10 Applicable Policy Concerning Wireless Technologies
In January of 2005, the EU set up a Working Party (6) on the protection of individuals with regard to the processing of personal data. This working party
has produced a paper that focused on RFID technology and data protection and looks at ways manufacturers and those who use such devices can
achieve legal compliance. The working party identified a number of circumstances where Rdata protection and privacy implications. These include
where RFID is used to store personal data. The paper also provides guidelines on the application of the EU data protection legislation to RFID devices
and tips on compliance.
In summary, these are:
• If information neither contains personal information nor is combined with personal data, then the provision of the data protection Directive
does not apply.
• When devices contain personal-data, there must be an in-built technical security mechanism to safeguard the privacy of the individual.
Examples include encryption and authentication protocols (e.g. ISO/IEC 9798). Authentication Keys such as DES and ECC can be used in
bots symmetric and asymmetric key distribution schemes.
• Compliance with the data protection principles by data controllers
• Data controllers must have a legal ground for processing the data - these are contained in Article 7 of the Directive.
• The individual must be provided with information in relation to the data controller, what processing is being undertaken and their rights of
access;
• Entitlement for the individual to have access to all the information a controller has on that individual;
• Obligation on controllers to implement appropriate technical and organisational measures to protect personal data against accidental or
unlawful destruction.
Essentially the policy document is just rehashing the directive and all the principles apply.
33.11 References
• 1. http://www.unhchr.ch/udhr/
• 2. "OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data".
http://www.oecd.org/document/18/0,3343,en_2649_34255_1815186_1_1_1_1,00.html
• 3. "Fair Information Practice Principles". US Federal Trade Commission. http://www.ftc.gov/reports/privacy3/fairinfo.shtm
• 4. http://www.spychips.com
• 5. http://ec.europa.eu/justice_home/fsj/privacy/docs/95-46-ce/dir1995-46_part1_en.pdf
• 6. http://ec.europa.eu/justice_home/fsj/privacy/docs/wpdocs/2005/wp105_en.pdf
• 7. "Scenarios for Ambient Intelligence in 2010". K. Ducatel, M. Bogdanowicz, et al., IST Advisory Group,Institute for Prospective Technological
Studies (IPTS), Seville,
2001. http://www.cordis.lu/ist/istag-reports.html
• 8. http://epic.org/privacy/rfid/rfidtestimony0704.html
• 9. http://www.cms.hhs.gov/HIPAAGenInfo/Downloads/HIPAALaw.pdf
• 10. http://www.ftc.gov/ogc/coppa1.htm
• 11. http://www.export.gov/safeharbor/
• 12. http://www.hl7.org/
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34 Ethics
General Description
Rapidly emerging innovative technologies have made prolonged independent community living possible for today?s geriatric population.(Mahoney,
2007). There is a need for regulating the vast amount of health information and websites offering various kinds of interventions for the sake of preserving
the integrity of medicine itself and the dignity of the patient wherein ethics plays a pivotal role. Ethical principles are essential since they provide the
basic framework for the development of regulations and guidelines that govern online health information and intervention programs. (Jimison, 2004).
Five ethical principles form the basis for evaluating moral conduct in health care: autonomy, veracity, beneficence, non-maleficence, and justice.
(Jimison, 2004)
? Autonomy: self-determination, right to privacy, individual freedom, fundamental to informed consent
? Veracity: telling the truth, keeping promises, open patient?physician relationship
? Beneficence: doing good, promoting the well-being of others, professional obligation to help those in need
? Non-maleficence: avoiding harm to others; protecting patients from danger, pain, and suffering (Hippocratic oath)
? Justice: fairness, respect for equality of all humans, equitable allocation of scarce resources, consideration of social policy.
The above mentioned principles have to be followed by all the stakeholders namely: Consumers/patients,Health care providers,government groups,
policy makers,web site developers and web site sponsors.
Issues
Some of the issues that have arisen with the advent of new technologies for provision of online health care are
? Privacy and security of the consumer?s medical information- This is an extremely important aspect of consumer autonomy and has to be safeguarded
in all circumstances.
? Quality and reliability of the information- Information found on the internet is dynamic, variable and often times inaccurate and it is extremely difficult for
a layman to judge the quality of the information provide online.
? Conflict of interest- Sites purporting to provide unbiased information may be violating ethics by selling or promoting medical products of the site
sponsors.
? Regulation and verification by experts ? It is difficult to regulate and verify information/interventions promoted by websites because of the large number
of websites and the ever changing material on them.
? Timeliness of information- Some websites use algorithms to update their date and time stamp automatically giving an impression of frequent review
whereas the actual data may be outdated.
? Electronic health records, electronic prescribing and decision support system- In this arena which is an integral part of home monitoring and
independent living, issues related to data ownership, data liability, informed consent to use, retrieval and access are of paramount importance and
needs to be incorporated within the appropriate legal and ethical codes. (Kluge, 1993).
? Personalized control and surveillance- Issues related to loss of personal freedom and control in cases where the caregiver has partial or total control
of the surveillance options need to be resolved. (Mahoney, 2007).
? New technologies- Ever changing technologies are creating new capabilities with their own ethical implications.
Research
A review article by Mahoney et al, found that though there is a lot of literature on home monitoring technologies and effectiveness of interventions, there
are very few studies focusing on ethical issues related to research or routine clinical practice. This review also came up with the most cited ethical
concerns as follows: ? Clarification of informed consent -50%
? Monitoring mechanisms-38%
? Privacy and confidentiality- 27%
? IRB approval- 26%
? Mechanism for contacting health care provider-22%.
The same author also mentions that there is a dearth of studies focusing on the impact of home health technologies on the lives of caregivers.
(Mahoney, 2007).
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Standards
There is an urgent need for the standardization of vendor applications to permit clinical data exchange for effective online health care delivery.
(Anderson, 2007).This has ethical implications as effective data transfer will minimize or prevent data errors and data leaks and reduce problems faced
in the areas of security and privacy.
Gaps
There is a gap between the technology developers who are not thoroughly familiar with the medical domain and may not be fully aware of humanistic
concerns and the reviewers with antitechnology biases who may cut short the development of some truly innovative and helpful innovations. (Mahoney,
2007).
Future Vision
Universal, objective, timely, relevant, accurate, good quality, peer reviewed, standardized online health care delivery with guaranteed security and
privacy with appropriate legal expression following standardized ethical codes and guidelines.
References:
1) Anderson, J., (2007). Social, ethical and legal barriers to e-health. Int J Med Inform, 76 (5-6), 480-3.
2) Jimison, H., (2004). Ethical issues in consumer health informatics. 143-149.
3) Kluge, E., (1993). Advanced patient records: some ethical and legal considerations touching medical information space. Methods Inf med, 32(2),
95-103.
4) Mahoney et al (2007). In-home monitoring of persons with dementia: Ethical guidelines for technology research and development. Alzheimer?s and
Dementia, 3, 217-226.
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35 Sports
35.1 Sports and Wellbeing
Maintaining a fit and healthy lifestyle is essential through all phases of life, starting as a child, moving into adulthood, and, finally, old age. As an adult
moves into the later years of life it is natural there will be a decline in health and ability to do certain activities. An important aspect of aging is enabling
independent living and a high standard of living. One factor used to assess this is the ability to perform Activities of Daily Living (ADL). This ensures that
someone is able to perform tasks such as washing and cooking that are essential for taking care of themselves. Using technology elderly people can be
more involved in the management of their own health and wellbeing. It can help modify attitudes and behaviour to improve and maintain a healthy
lifestyle through positive feedback and motivational instruction. Those suffering from chronic diseases such as morbid obesity, COPD, diabetes, and
stroke, could use this technology to help make positive changes in their lives by changing diet, increasing exercise, and following rehabilitation
treatments.
Even in later life, sports can be continued and enjoyed by the elderly. By keeping up physical fitness the risks of falling or sustaining injury can be
reduced, however, staying motivated to exercise, measuring performance and ensuring good technique is vital. Monitoring and positive feedback can be
used to measure improvements due to exercise, correct technique, and provide motivation to continue a program. Running, walking and rowing are
examples of some of the sports that would benefit through monitoring.
During old age the chances of suffering from a chronic disease or falling increases. As a part of their recovery or treatment, physiotherapy is often
prescribed to aid recovery, strengthen weakened muscles, and help rebuild confidence. While in hospital, following a program of physiotherapy is
simple, as the frequency and content of the rehabilitation program is decided for the patient taking them through the exercises, offering encouragement,
and helping correct technique. When a patient is discharged, maintain the course of physiotherapy can be challenging. Using similar techniques as
those used to monitor sports; physiotherapy can also be monitored and managed, building a solid fitness base for recovery and increased fitness.
35.2 Sport Application Examples
35.2.1 Nutrition and Training Monitoring
By keeping a log of all food eaten and the activities, daily calories compared to energy expenditure can be calculated ensuring the optimum amount of
calories is being consumed compared with the amount of daily exercise. This can also be used to customise a training program to help promote a
balance between heavy and lighter training sessions. This will help build a good fitness base and promote fast recovery and improvement without injury.
35.2.2 Running / Walking
Running and walking are excellent for managing weight, improving endurance, and sustaining a healthy lifestyle. For the elderly, walking is particularly
beneficial as it is low impact and requires no equipment, apart from either a pair of running or walking shoes. Measuring heart rate and step length
during an exercise session can provide information relevant for estimating the distance travelled, speed, heart rate in beat per minute and calories burnt.
These can be then incorporated to into a training program to build fitness and provide feedback to the user.
35.2.3 Rowing
Rowing can provide a good all round fitness sport promoting muscle strengthening with flexibility and a cardiovascular work out. It is also relatively low
impact and as such ideal for the elderly. Maintaining good posture and technique is vital for avoiding injury and maximising benefit. Traditionally a coach
would observe from the bank suggesting changes in technique, however, this is not practical on a day to day basis. Wireless sensor networks (WSNs),
for on-water rowing, and ambient sensors, for indoor rowing on a ergometer, could be used to monitor posture and suggest changes in technique.
35.3 Monitoring
There are many ways in which activity can be monitored depending on the type of data that is required. These could include; biomechanical, ambient,
respiratory, and circulatory sensors. Home and mobile systems and WSNs can provide platforms for incorporating sensors and collecting data in a
non-intrusive way.
35.3.1 Biomechanical
There are several sensors that are suitable for measuring local body motions, such as those made by the limbs. To measure the angular change of joint
angles, Electric Goniometers (EGs) can be used which provide a varying voltage output depending on the change in angle. While these sensors are
cheap, they may prove awkward to attach by the user, especially if elderly, to be able to measure the relevant angles accurately.
Other sensors that have been used to measure motion are MEMS (MicroElectroMechanical System) accelerometers and gyroscopes, because of the
decreasing size, cost, and energy requirements. Accelerometers have been used extensively, often placed on the limbs, to determine the limb motion.
One of the problems of using accelerometers is that they measure both static acceleration with respect to gravity and dynamic acceleration. These can
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be hard to separate without additional information; however, dynamic acceleration can provide useful information regarding the impact of the body on
other surfaces. Accelerometers can also be used to provide information on the bodies sway and general biomechanical motion.
MEMS gyroscopes have also been used to capture the motion of the body due to the issues involved in using accelerometers. By providing angular
velocity information the rotation of the limbs can be found. Both commercial companies and the research community have used gyroscopes extensively
to measure the body?s movements.
For applications such as running and walking force plates can be used to analyse the gait cycle. From this the distribution of the weight over the foot can
be found and provide useful information regarding rehabilitation, the use of walking aids, and walking and running style. Conventionally force plates are
incorporated into the floor limiting their range of use; however, developments in force sensors could provide for a portable version in the future that can
be inserted into the shoe such as the Paro Tech insole product.
35.3.2 Ambient
Detailed analysis of human motion can be captured using ambient or visual systems. These offer a wireless non-intrusive method for monitoring sports
performance without interfering with the activity. There are many systems that are commercially on the market that use imaging systems, such as
infrared cameras, to detect markers placed on the joints to track the motion of the user. One of the drawbacks to these types of system is the necessity
of many cameras, with a range of only a couple of meters, and use of multiple markers which increase the complexity of the tracking and can suffer from
occlusion.
If a high degree of detail is not required for motion analysis there are several alternative methods of motion capture that could be used. Passive Infra
Red (PIR) sensors can be used to detect general motion in a room providing a high degree of privacy, but offering very limited motion resolution.
Alternatively, a device called a blob sensor can be used to capture image data from a room and then convert the information into a binary image using a
statistical model, background segmentation. Using the blob sensor, global pose can be derived; however, self occlusion can cause incorrect blob sensor
results. Higher motion resolution can be found by using optical flow to capture motion within the blob.
35.3.3 Cardiovascular and Respiratory
Monitoring the cardiovascular system can provide valuable information related to general fitness and the health of the user. Electrocardiographs (ECG)
measure the electrical activity of the heart through electrodes attached to the chest. ECG is often used to determine damage to the heart or the onset of
disease. Implantable versions are also available for monitoring episodic events. Photoplethysmography (PPG) can also be used to monitor cardiac
rhythm.
To measure respiration, spirometry can be used to measure the rate of the air transferred through the lungs, and the volume. This test can be used to
determine the quality of the respiratory function for those suffering from Chronic Obstructive Pulmonary Disorder (COPD), asthma, emphysema, and
cystic fibrosis, as well as athletes. Breath rate has also been measured using piezoresistive sensors, which provide a varying output as the force on
them changes, attached across the chest.
35.4 Challenges and Issues
To develop systems that can be used to monitor and provide valuable feedback to the user regarding the performance of a given sport or activity, there
are several challenges and issues that need to be addressed, many of which are common to other CAPSILs. These include: biosensor and platform
development, power management, data modelling and inference, and user interfacing.
35.4.1 Biosensor and Platform Development
To capture the required information for sports activity monitoring, both a suitable sensors need to be developed and a wearable platform to interface with
the sensors. Sensors that would be used for activity monitoring need to be small, light, non-intrusive, and easy to attach. Incorporating sensors into
clothing is one method of attaching sensors. This presents challenges such as ensuring the position of the sensors does not change, making the
sensors either detachable or robust to washing and designing garments that are specific to the user?s dimensions. If sensors are to be attached directly
to the body they need to be made from biocompatible materials that will not irritate or harm the skin or user. In terms of platform development there are
a number of WSN platforms available depending on the deployment and type of sensors used.
35.4.2 Power Management
For any wireless system that will be deployed and expected to run autonomously for extended periods of time, power management becomes an
important issue. Replacing and recharging batteries may not be possible or practical if sensors are implanted or hard to access. For implanted devices,
induction coils could provide a means of recharging; however this would only be possible for implants near the surface of the skin. This has led to
research into the field of energy harvesting to provide a solution which does not require external intervention. Current energy harvesting techniques
include use of temperature difference, motion, and biological sources to generate power.
35.4.3 Data Modelling and Inference
In terms of data modelling and inference there are many different research areas including sensor placement, feature selection, data modelling and
inference. Also, when multiple sensors will be used, synchronisation is an important consideration.
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• Sensor Placement & Feature Selection: The question of which sensors to use, where, and how to deploy them is vital to acquire the
relevant information for monitoring a specific activity or sport. Sensors could be deployed on the body or within the surrounding area. In terms
of sports, ambient sensors provide a good solution as they are not attached to the body and as such would not obstruct the user in any way;
however, they do require the user to stay within there area of operation. Wearable sensors on the other hand have to be attached to the user,
but offer more freedom of movement. Another consideration regarding sensor placement is the amount of similar data required. By collecting
similar data robust algorithms can be used to compensate for the failure of a sensor such that the quality of the end result does not suffer.
This would however increase the number of sensors and the amount of data collected which in turn would have a direct impact on the
processing time, energy expenditure and communicational costs. As well as sensor placement, the features extracted from the raw sensor
data to monitor sports can be investigated. By finding the features that can best describe or distinguish features the overall amount of data
can be reduced. As with the sensor placement, it may be beneficial to build in redundancy for important features and or remove those that are
providing too much repletion.
• Data Modelling & Inference: To successfully model the sport being conducted, it is important to have detailed knowledge regarding the
activity such that relevant data can be collected and correctly interpreted. Due to the complexity of many sporting or rehabilitation activities,
this is especially important. Once data has been collected and features extracted, classification or clustering algorithms can be used for
inference. Modelling algorithms can be used to extract underlying patterns from the data observation; however, the choice of algorithm is
dependent on the type of data, how the data will be processed, and the amount of data. Popular algorithms include Bayesian Networks that
assume prior knowledge, and Conditional Random Fields (CRFs) and Hidden Markov Model (HMM), which are temporal. The latter two are
able to model multi-stage activities that evolve over time. Classification algorithms for inference have traditionally been centralised and applied
after the data has been collected and combined from all sources. With the increasing computational power of sensor nodes and the need for
real-time evaluation and feedback the trend is now towards distributed systems. This removes the need for a centralised node and can be
more easily scaled.
35.4.4 Interfacing with the User
A crucial challenge for sports monitoring, is making the system as easy to use as possible. This especially key if the potential users are the elderly, who
may not be as technologically inclined. Any monitoring system would need to have the following attributes to make them practical for deployment:
self-managing, self-healing, autonomic, and be contextually aware.
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36 Business Models
36.1 Review of Reimbursement Models for Telehealthcare
The CASPILs on Mobile and Home Monitoring Systems and the Wiki on Government Policy identified reimbursement for telehealth as the single biggest
barrier to its broader adoption. It also identified that the geographical landscape is one of fragmented pilots that operate very much in standalone mode
with little or no connectivity. The major efforts within reimbursement for telehealthcare have been in the USA and are on a fee-per-service basis. There
does not currently exist a blanket coverage reimbursement model and any reimbursements are on a 'pay as you go' approach. Progress has been slow
however and according to a recent national survey of almost 1,000 home care agencies, only 17.1% reported that they presently use a telehealth
system (5). Europe is a mish-mash of different initiatives at present but none going much further than pilot activity.
36.2 USA Telehealthcare Reimbursement Models
The demand for home healthcare services in the U.S. has increased 20% per year for the last ten years and is expected to continue at this rate (1).
According to the Association for Home Care and Hospice... "Approximately 7.6 million Americans currently receive home care because of acute illness,
long-term health conditions, permanent disability, or terminal illness" (2). This increased demand for home care has led to a large increase in home care
spending. Note: Home Care as defined here is not the same thing as telehealth or telemonitoring although these categories do qualify as
sub-categories. Medicare is the largest single payer of home healthcare services and, in 2006, its spending accounted for approximately 37% of home
health expenditures (2). Medicare's home health spending was anticipated to grow 13.7% in 2007, with an average a 10.2% growth rate per year from
2008 to 2017 (3). Further detail on Healthcare Reimbursement in the USA.
Currently 35 states reimburse for Medicaid including Alabama, Alaska, Arizona, Arkansas, California, Colorado, Georgia, Hawaii, Illinois, Indiana, Iowa,
Kansas, Kentucky, Louisiana, Maine, Michigan, Minnesota, Missouri, Montana, Nebraska, Nevada, North Carolina, North Dakota, Oklahoma, Oregon,
South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin and Wyoming.
36.2.1 The Veterans Administration
The Veterans administration has been leading the reimbursement efforts in the USA. More details at The Veterans Administration page.
36.2.2 Examples of US Reimbursement Policies by State
• California
• Texas
• Minnesota
36.2.3 USA Reimbursement Models - Summary Points
• Specifics are called out such as Fee-for-service which is an important aspect of any telehealth business model. That is to say the actual
consultation allowed must be specified exactly (a scheduled consultation just in the face to face model) or remibursement will not be possible.
Note in the Texas example that they have extended the allowable range of practitioners that are allowed to bill. This is a good example of the
detail that is contained in a telehealth policy and also of the variance that can occur between states/countries.
• The Consultation codes that will be used in a billing system (and ultimately appear as a HL7 message, see CAPSIL on Standards) differ from
state to state. This can lead to interoperability problems if a pan-national solution is to be put in to place. Also it complicates affairs if a
teleconsultation is sought in a state out side of the state where the person resides (which codes to use?).
A Texas-based organisation called PACE Healthcare (9) operates a home healthcare agency which is approved by Medicare. The PACE service
involves mainly a nurse or other clinical staff actually visiting the persons and providing a face to face consultation. However they do advertise some
telehealth home monotoring. It is an interesting model and may point the way forward for widespread adoption of telehealthcare i.e. telehealthcare in
conjunction with face to face visits.
There is quite a bit less information on the situation regarding private payers and telehealth although some states in the US (California, Louisiana and
Texas) do have legislation to cover reimbursement.
36.3 European Telehealth Reimbursement Models
In terms of telehealthcare reimbursement, there is very little of any substance happening in Europe. Most efforts are part of a 'pilot' program and are
often governed by local authorities within the member states. Thus there is really no national or pan-national initiatives in place. Geremany does provide
some reimbursement for chronic conditions however they also charge a 14% income levy for healthcare costs. The reimbursement landscape is very
much operated on a region-by-region and program-by-program basis which indicates the need for an overall structure and clear EU-wide policy.
36.4 Examples of European Reimbursement Policies by Country
• Germany
• Sweden
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• Bulgaria
• Denmark
• Finland
• UK
• Ireland
36.5 Telehealthcare Business Models - The Opportunity and Markets
The Analyst firm Datamonitor published a report in 2007 (10) that predicts "the home telehealth market will grow at a five-year compound annual growth
rate of 56 percent, compared to only 9.9 percent growth in the clinical market". The Datamonitor report goes on to say "Telehealth's Increasing Role in
Healthcare, expects that the overall global telehealth market will exceed $8 billion by 2012".
It is generally recognised that this market will follow the areas of most need and highest return of investment (chronic disease programs) initially until a
comprehensive picture of the benefits of telehealth and in particular, home monitoring is built up. Once this happens the general public will come on
board and so the market will continue to grow. Key to adoption by the wider public will be Digital Health Records or personal health records as the case
may be.
This is borne out by the fact that at present many telehealth devices are available to the genertal population (blood pressure, ECG, glucose) however
data shows that market penetration has been very weak. This is due to factors discussed in the Government Policy WIKI and CAPSIL and includes
things such as lack of reimbursement, lack of understanding of the benefits, interoperability etc.
36.6 Chronic Disease Programs - The Way Forward
Chronic illness is a disease or condition that lasts for a long period of time or is marked by frequent recurrence; for instance, congestive heart failure
(CHF), diabetes, or asthma. According to the Information Technology Association of America E-Health Committee "Today, 45% of the American
population is affected by one or more chronic illnesses. Studies show that caring for people with chronic disease consumes approximately 78% of all
healthcare spending in the United States-more than $1 trillion annually" (7). The World Health Organization. projects that chronic disease will be the
leading cause of disability by 2020 and will be the most expensive problem facing healthcare systems (8). Researchers have begun to conclude that
telehealth works best with those patients who need the most frequent contact, such as those with chronic diseases (1).
Patients with chronic diseases require more frequent visits to the emergency room, incur a higher rate of hospitalizations, have a higher risk of being
institutionalized, and are financially more costly than the 'average' patient. On discharge from the hospital to their homes, patients with chronic disease
often need a great deal of support including compliance with medications and treatments, improved health behavior coaching, and symptom
management.
Telehealth technology allows care providers to monitor the patient daily and make real-time identifications and interventions in the care of their patients.
These early interventions are vital to the improvement of symptom management and reduction in unnecessary health care encounters such as
hospitalization or emergency room visits. Remote monitoring can lead to better symptom management, improved health behaviors, and compliance with
medications and treatments. Furthermore, the operational efficiencies created by telehealth can have a financial benefit to the care agency (often the
taxpayer in the case of public health services).
36.7 Telehealth Business Models Are Not Just About Vitals Measurements
A lot of focus to date has been about getting devices that can take vital measurements (ECG, blood pressure, glucose etc) on to the market and also
about getting standards of interoperability in place (see CAPSIL on Standards). However a proper business model for telehealthcare will be about much
more than just vitals measurements. It is essential that telehealthcare involve regular communications be it over videoconferencing or teleconferencing
or other methods. Particurlarly for older people, maintanance of the doctor/patient relationship needs to be kept as 'personal' as possible and not
become a barrier to the human to human interface.
36.8 Examples of Telehealth Solutions That Preserve the Human to Human Relationship
• Intel's HealthGuide (11)
• Viterion TeleHealth Network (12)
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37 Proactive Models of Telehealthcare
It is generally accepted that areas of disease management will be the first major opportunity for telehealthcare and telememonitoring as the business
case and return of investment is strongest there at present. However the next major opportunity will be in the area of proactive or preventitive
healthcare. ?Proactive healthcare? is concerned with learning ?normal? patterns of daily activity and then determining any deviations from this
?normal?. When a deviation is detected, an alarm is raised and some action taken such as contacting a caregiver. The concept is based on early
detection of events and behaviours and proactive correction, thus preventing situations from progressing and becoming acute medical conditions. For
example if a person normally visits the bathroom once per night and suddenly this goes to nine or ten times, it may indicate a serious bladder condition
that if acted on early, may prevent a much more serious outcome. Proactive healthcare can be based around older people, where monitoring activity (or
inactivity), medication compliance, mobility or cognitive behaviours can prevent a further condition from arising and becoming acute. An example is
where a person forgets to take medication or takes too much (poor eyesight for example) which can lead to diziness and ultimately a fall. However
Proactive Healthcare is also about using telemonitoring for the so called 'worried well' and being an aid towards a healthier and more balanced lifestyle
(which may prevent chronic diseases later in life). An example would be around weight management, where interactive sessions and feedback with
clinicans and dieticians including motivational programs, may prevent Obesity.
37.1 Example of 'Proactive' Healthcare System
A newly available commercial solution aimed at monitoring home behavioural patterns for older people is the GE QuietCare system (13) which uses
multiple motion detectors connected to a central ?learning server?.
• See GE QuietCare system page for details.
37.2 Business Model Needs to Include Some Form of Assessment for Suitability
It is not a given that if someone is willing to sign up for a telehealthcare program, that they should be allowed to participate! The need for patient and
environment assessment for appropriateness of home telehealth is very important if telehealth is expected to work beneficially and not cause more
problems than it is intended to overcome! Further detail on this can be found in the patient and environment assessment page.
37.3 References
• 1. Kinsella A. Home Telehealthcare: Process, Policy, & Procedure Sunriver, OR: Information for Tomorrow, 2003
• 2. National Association for Home Care & Hospice. Basic Statistics About Home Care. NAHC Web site., 2008
• 3. Centers for Medicare and Medicaid Services. National Health Expenditure Projections 2007-2017. CMS Web site., October, 2007
• 4. http://www.americantelemed.org/files/public/policy/LegislativeAlert_16July2008.pdf
• 5. Fazzi Associates. Philips National Study on the Future of Technology And Telehealth in Home Care.: Philips Consumer Healthcare
Solutions., April, 2008
• 6. http://tie.telemed.org/articles/Patient_Assessment.pdf
• 7. Information Technology Association of America E-Health Committee. Chronic care improvement: How Medicare transformation can save
lives, save money and stimulate an emerging technology industry. Information Technology Association of America, May, 2004
• 8. Belfield G , Colin-Thome D. Improving chronic disease management. Department of Health, UK National Health Service, March 3, 2004
• 9. http://www.pacehealthcare.com/
• 10. http://www.baypines.va.gov/services/ccht.asp
• 11. http://www.intel.com/healthcare/ps/healthguide/index.htm
• 12. http://www.viterion.com/web_docs/V200%20Ad.pdf
• 13. http://www.gehealthcare.com/usen/telehealth/quietcare/proactive_eldercare_technology.html
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38 Government Policy
Telemedicine can be defined as a delivery of healthcare services through the use of Information and Communication Technologies (ICT) in a situation
where the participants are not at the same location. The participants can either be two health care professionals (e.g. teleradiology, telesurgery) or a
health care professional and a patient (e.g. telemonitoring of a chronically ill patient such as those with diabetes and heart conditions, telepsychiatry,
etc). Literature and case studies of already-implemented or piloted telemedicine applications (including many EU-funded projects) report benefits at
different levels. For example, disease management through telemonitoring of heart conditions reduces mortality rates by an estimated 20%. Savings
estimates due to telemonitoring of patients who would otherwise be kept in hospitals have been shown to range between 30-60% and 40-70% of health
professionals' time. The global market for telemedicine, could increase from ?4.7 billion today to over ?11.2 billion by 2012, an average annual growth
rate of 19%. In the context of an ageing population, increased burden of chronic diseases, active participation of more demanding patients and ever
increasing health expenditures, the realisation and amplification of telemedicine services is important and urgent.
However a number of barriers currently limit the deployment of telemedicine solutions to the general population and it is imperative that these barriers be
removed before the real revolution of telemedicine and in particular home monitoring solutions take off.
As with other technology deployments (e.g. broadband), Government policy and intervention is key to the widespread proliferation and realisation of the
full societal benefits. Government's job is to ensure that access to beneficial technologies such as home monitoring solutions are accessible to all (not
just the privileged) and that it is affordable and reliable. Technology companies can bring products to the table and these can work well, however without
coherent national policy regarding its use, efficiacy, privacy, security and ethics and legal regulation, the technology may well sit on a shelf and never
see its true potential. This is the crucial role that government can play and it is imperative that they do get involved and not leave it to private
stakeholders.
38.1 Barriers to Telehealth Adoption Where Government Policy Can Help
38.2 Cost and Reimbursement
The lack of cost and reimbursement policies have a major negative influence on the adoption adoption of home health solutions (1). Further details can
be found in the Cost and Reimbursement section.
38.2.1 Situation In the US
In the US, reimbursement is on a Fee-for-service basis. That is to say the actual consultation allowed must be specified exactly (a scheduled
consultation just in the face to face model) or remibursement will not be possible. The Consultation codes that will be used in a billing system (and
ultimately appear as a HL7 message, see CAPSIL on Standards) differ from state to state. This can lead to interoperability problems if a pan-national
solution is to be put in to place. Also it complicates affairs if a teleconsultation is sought in a state out side of the state where the person resides (which
codes to use?). Further detail on Healthcare Reimbursement in the USA.
38.2.2 Situation in Europe
See Wiki on Business Models for more information on the European situation on a country by country basis.
In Europe the picture is very fragmented with small scale pilots being the order of the day and little or no reimbursement policies or joined up thinking
across countries. Further detail is available on the European Reimbursement Situation page.
38.3 Demonstrating the Benefits - Large Scale Pilots
Very generally we can say that there are two main benefits to be derived from telehealth monitoring;
• Personal and Societal Health and Wellbeing benefits
• Financial return for health services (public and private) and individuals (assuming reimbursement policies are in place).
A major obstacle towards widespread proliferation of telehealth services is the lack of hard data to indicate the business case.
What is needed to demonstrate both these aspects are large scale (ideally national scale) pilots that calculate precisely the Return of Investment (ROI)
data based on these two tenets. For the reasons specified here (legal, privacy, cost etc) organisations are not willing to move forward with large scale
pilots and so efforts tend to be 'sandboxed' and locked-down, fragmented efforts. Government can help here. They can make a landscape 'safe' from a
legal perspective which removes a major hurlde to participation from the medical profession. They can also incentivise individuals to participate through
cost reimbursement schemes such as tax-breaks, and finally they can fund significant portions of the pilot, which takes some pressure off industry
having to shoulder all the cost. Further, if the government manages the administration of such a pilot and works through standard procurement
processes, the process can be seen to be open, fair and transparent i.e. no favours given to any one technology or healthcare provider. This can be
important in winning the public perception that this is not just another commercial push, but one that can genuinely benefit ordinary people. An excellent
example of such a pilot scheme is detailed here (2), where during the summer of 2009 in the UK areas of Kent, Newham and Cornwall (which cover
more than a million people between them) will be installed telecare and telehealth devices in around 7,000 homes, to assess the impact of assistive
technology both on people?s lives and on the cost of providing care and support.
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38.4 Data Ownership and Legal Concerns
A major issue in the uptake of home healthcare monitoring is that of policy variation between state and country (even sometimes at county level). Take
an example of a person is being monitored in France and the clinician residing in Germany; then the question arises ? ?which country?s law is
responsible for the integrity of the consultation (accurate, secure, private etc)? Where is the accountability and where is the enforcement if this contract
is breached? ?.
The question of data ownership and jurisdiction (i.e. where is the medicine actually being practised ?) needs to be addressed worldwide. For example
within the USA with the current licensing policies, it is impossible for a patient to seek care from a physician that does not practice in the patient?s state.
According to the Office for the Advancement of Telehealth (OAT) (3) ?A patient in Oregon could not be treated remotely by a New York doctor, even if
that physician were the country?s foremost expert on the patient?s disease.? ?This is detrimental to the patient?s health because the individual might
not receive the best care possible?, and according to Intel?s Eric Dishman, ?it hinders telemedicine?
38.5 Perception and Attitudes
There exists a ?digital divide? between people aged 38 to 59 and people over 60 years of age in their attitude and confidence using technology. This is
a statement that not only applies to the field of healthcare but to technology in general. However in the field of healthcare, being a primal concern for
most people, this divide is most pronounced. These psychological barriers need to be addressed if home health monitoring of seniors is to maximise its
potential societal benefit. Issues such as proper (user friendly and intuitive) design of systems, guaranteed security features, personal health records
and privacy legislation can help this cause greatly.
Research findings suggest that the use of remote home health monitoring equipment can lead to certain apprehensions in older patients who want to
sustain their personal relationships with family and doctors. Older patients may also resist using home health care monitoring services to avoid ceding
authority to their adult children. Some fears held by older adults are possibly a response to societal ?ageism.? Society stigmatizes signs of aging and
weakness, which some older patients feel are enhanced through the public use of monitors. Another issue for older people will be the elimination of
face-to-face care and this may create a perception that there will be a reduction of social interaction in the older person?s life.
The lack of motivation in older people to use telemedicine in the home often stems from an inability to understand how the technology will benefit them
and make dramatic improvements in the quality of their life. It has been suggested that one reason for this is that they are ?present-oriented? and less
willing to spend their time in an unpleasant way for a future goal. The technology also needs to be user friendly and practical. How to set it up and use it
must be almost transparent to the person?s daily routine, and finally the cost and reimbursement issues must be almost pervasive in nature and require
very little beaurocracy and set up/sustaining time.
38.6 Privacy, Security and Ethics
Home healthcare monitoring poses the fundamental problems of security and privacy balanced with safe and effective healthcare. In reality these can be
two opposing ideas as the more effective the home healthcare package, the more threat to privacy that exists. A full treatment of the issues is given in
the Privacy, Security and Ethics page.
38.7 Broadband Proliferation
Broadband access technology of whatever type (DSL, WiMAX, Satellite etc) is a key enabler technology for home healthcare monitoring, particularly if
personal health records are to be employed. The three important points in discussing broadband are Coverage, Speed and Cost. These are dealt with in
detail in the Broadband Proliferation page.
38.8 How Organisations Attempt to Influence Public Policy
According to the US Government web site (4) organizations and institutions attempt to influence policy and public opinion in a variety of ways:
• Educating public officials and their staffs about the positive or negative effects of policy proposals
• Conducting advertising campaigns and public relations initiatives supporting their views
• Arranging for expert opinions and providing facts, data and opinion polls to support their positions
• Encouraging their members to vote, communicate with their elected officials and write letters to the media supporting their positions;
• Forming political action committees to contribute money to the campaigns of candidates who support their positions.
Groups such as Special Interest Groups, Non Governmental Organisations, Public Policy Associations and Trade Associations as well as individual
businesses all work to influence government policy through various means. Further detail on these organisations and the methods they use can be
found on the How Organisations Attempt to Influence Public Policy page.
38.9 Government Intervention
Government Intervention happens when a government takes a direct role in the enhancement of economic efficiency by addressing problems with the
operation of markets and institutions or the achievement of a social objective, such as promoting equity and wellbeing.
Various governments will have their own definitions around when intervention is appropriate however a fairly representative example of Government
intervention policy in the developed world would be the UK Government which uses The HM Treasury Green Book (5) to identify when it will intervene.
See the UK Government Intervention Page for full details.
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38.10 Notable Examples of Government Policy and Intervention for Societal Benefit
Two good examples of where governments have intervened directly for greater societal benefit include;
• Smoking Cessation
• Asbestos
38.11 References
• 1. http://www.americantelemed.org/i4a/pages/index.cfm?pageID=3334
• 2. http://www.ehealtheurope.net/comment_and_analysis/278/2008:_the_year_telecare_grows_up_tcq
• 3. Kumekawa, Joanne K. (September 30, 2001) ?Health Information Privacy Protection: Crisis or Common Sense?" Online Journal of Issues
in Nursing. Vol. #6 No. #3
• 4. http://www.america.gov
• 5. http://interactive.cabinetoffice.gov.uk/strategy/survivalguide/skills/ao_rationale.htm
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39 Descriptive Capsil
The start of a descriptive CAPSIL begins with a general description of the order of 3 paragraphs with many links.
39.1 Issues
• (problems/challenges) being addressed in this are - some cross cutting
39.2 Justification
39.3 Scientific (Basis/efficacy/evidence)
39.4 Research
39.4.1 Players
39.4.2 Projects
39.4.3 Funding
39.5 Commercial
39.5.1 Products
39.5.2 Players
(links to VCs/Angels/Agencies/MNCs/SMEs)
39.5.3 Procurement
39.6 Standards
39.7 Gaps
39.8 Future Vision
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40 Linking Capsil
The start of a linking CAPSIL begins with a general description of the order of 3 paragraphs with many links. This general decription is followed by a
series of sections or a single table with links to projects/systems/tools. This type of CAPSIL is a resource to help create common point in the Wiki to
enhahce the overall utilitiy of this Wiki.
The table's caption
Column heading 1
Column heading 2
Column heading 3
Row heading 1
Cell 2
Cell 3
Row heading A
Cell B
Cell C
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41 White Papers
This pages contains links to white paper documents in this Wiki or online elsewhere related to the state of aging in the EU, US and Japan.
41.1 EU White Papers
Overview of the Technology Research for Independent Living Centre Ireland
41.2 US White Papers
41.3 Japan White Papers
In English WHITE PAPER ON THE AGING SOCIETY (SUMMARY) FY 2007 The State of Aging and Implementation of Measures for an Aging Society
in FY 2006 Measures for an Aging Society in FY 2008
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42 ICT and Ageing Deployments
42.1 Retrofits
42.2 New Builds
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43 Workshops, Conferences and Portals
43.1 Conferences
43.1.1 Health
• ICT-BIO 2008 Computer modelling and simulation for improving human health Brussels, Belgium 23-24 October, 2008
The objectives of this conference are: to communicate the opportunities and challenges of the Virtual Physiological Human and Integrative System
Biology and other topics that lie at the crossroads of ICT, Biology and Medicine; to provide the possibility for discussions on progress beyond the current
state of the art and to promote international cooperation and networking; to provide an overview about the current activities in these domains not only in
EU but also in US and other countries, and to examine opportunities for closer cooperation between EU and US funding agencies.
• eHealth 2008 "eHealth without frontiers" Portoroz, Slovenia May 6-7, 2008
The conference will focus on borderless eHealth in both its vertical and horizontal aspects that cover a wide range of locations and stakeholders. The
?without frontiers? theme highlights among others: the collaborative work being done on good practices in eHealth, the focus on cross-border healthcare
provision, ongoing proposals on eHealth interoperability, and the integrating work to be undertaken in the future in the prospective eHealth large-scale
pilot and new telemedicine initiatives.
• EUnetHTA Conference 2008: HTA?s Future in Europe Pasteur Institute, Paris, France November 20, 2008
Health technology assessment (HTA) is a multidisciplinary process that summarises information about the medical, social, economic and ethical issues
related to the use of a health technology in a systematic, transparent, unbiased, robust manner. Its aim is to inform the formulation of safe, effective,
health policies that are patient focused and seek to achieve best value.
43.1.2 Gerontechnology
• ISG '08 International Conference of the International Society for Gerontology Pisa, Italy June 4-7 2008
A forum where all the people involved in activities on gerontechnology (robotics researchers, architects, biomedical engineers, neuroscientists,
biologists, experts in political science and economics, and scientists from many other communities) will meet and interact, present results and discuss
about future research lines.
• PETRA 08 1st International Conference on Pervasive Technologies Related to Assistive Environments Athens, Greece July 15-19 2008
The PETRA Conference brings together different types of technology to address an important social and healthcare issue: as the world's population
ages, there is growing interest in solutions for the inhome care of the elderly as well as for the care of people with Alzheimer's, Parkinson's and other
disabilities or traumas.
• IET Assisted Living 09Aims of the conference:
-To review recent innovations and developments within Assisted Living as well as related e-health technologies
-To report on the latest research findings
-To bring together researchers, industrialists, policy makers and service providers within the areas of the technical scope
• International Association of Homes and Services for the Ageing - 19th-22nd July, London. The theme of the conference is 'Leadership beyond
Borders' and comprises keynote lectures, workshop sessions, a design showcase and poster presentations.
43.1.3 Pervasive and Ubiqutious Computing
• 3rd International Conference on Pervasive Computing Technologies for Healthcare 2009
Pervasive healthcare may be defined from two perspectives. First, it is the development and application of pervasive computing (or ubiquitous
computing, ambient intelligence) technologies for healthcare, health and wellness management. Second, it seeks to make healthcare available to
anyone, anytime, and anywhere by removing locational, time and other restraints while increasing both the coverage and quality of healthcare.
• ICPS'08 : International Conference on Pervasive Services Sorrento, Italy July 6-10 2008
The 2008 International Conference on Pervasive Services (ICPS'2008), to be held in Sorrento, Italy, provides a forum for researchers, engineers,
application & service developers and users to present their latest advances in the field of pervasive services. Use cases and usage models for these
pervasive services are of particular interest to the conference.
• BSN 2009 6th International Workshop on Wearable and Implantable Body Sensor Networks. Berkeley, CA, USA. date: June 3 - 5, 2009.
BSN 2009 is the sixth workshop following the successful BSN workshops held at Imperial College in London UK (2004, 2005), MIT in Boston USA
(2006), RWTH Aachen University in Aachen Germany (2007), and Chinese University in Hong Kong China (2008).
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• 6th international workshop on Wearable Micro and Nanosystems for Personalised Health
Oslo, Norway, 24-26 June 2009
43.1.4 Robotics
• Human-Robot Interaction 2009 San Diego, USA, March 11-13 2009
The Fourth Annual Conference on Human-Robot Interaction is dedicated to the advancement of natural human-robot interaction, which highlights the
importance of the technical and social issues underlying future long-term human-robot interaction, in the context of companion and assistive robots for
long-term use in everyday life and work activities.
• HRI2010 Nara, Japan, March 2-5 2010
5th ACM/IEEE International Conference on Human-Robot Interaction
43.1.5 HCI
• USAB 2009 ? Usability & HCI for eInclusion Linz, Austria 09 Nov 2009 ? 10 Nov 2009
• International Conference on Affective Computing and Intelligent Interaction Amsterdam, Netherlands, September 10-12 2009,
This year, we are especially soliciting papers discussing Enabling Behavioral and Socially-Aware Human-Machine Interfaces in areas including
psychology and cognition of affective and social behaviour in HCI, affective and social behaviour analysis and synthesis, affective and social robotics.
• Virtual Environments, Human-computer Interfaces and Measurement Systems Hong Kong, China, May 11-13
VECIMS 2009 is an interdisciplinary symposium that intends to bring a methodical instrumentation and measurement perspective to the theory and
practice of virtual and virtualized environments, and human-computer interfaces by focusing on the quantitative and metrological aspect of these
technologies and their interactive applications in telerobotics, telemedicine, remote control, engineering design, environment sensing and monitoring,
training, education, arts, and computer games.
• International Cross-Disciplinary Conference on Web Accessibility Madrid 2009, 20-21 April 2009
An ageing but Web literate population indicates a large market for online shopping and services especially when mobility is a problem for the shopper. In
this case we wonder how this new population will interact with Web based resources, and what new problems in accessibility will there be to overcome?
43.1.6 Biotechnology and Biomedicine
• BIOT-2008 Biotechnology and Bioinformatics Symposium
Research and development in biotechnology requires the collaboration of scientists and engineers in the fields of biology, chemistry, computer science,
chemical engineering, and electrical engineering. This symposium brings together scientists, engineers and scholars from relevant fields with
practitioners from industry in order to help each group to understand progress made in the area as a whole.
• 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'09) Minneapolis, Minnesota, USA
September 2~6, 2009
This event allows people to report on significant findings and developments in all the major fields of biomedical engineering, and discuss government
and industry related issues.
43.1.7 Inclusive Design
• Include 2009
International conference on Inclusive Design Royal College of Art, London, UK, 5-8 April 2009 Inclusive design into innovation: transforming practice in
design, research and business
43.2 Portals
• EU CORDIS: Community Research and Development Information Service
Portal for European Framework Programme funded research and projects.
• eHealth News.eu
The main service provided by eHealthNews.EU portal is the dissemination of the European eHealth related news articles. Our news service is covering
six major areas, Industry news, Research News, Conferences News, Open Calls, Publications and White Papers.
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• @HEALTH
@HEALTH is a community of European and Latin American research organisations, industries and public administrations interested in eHealth
technologies, innovation and applications. This community favours the dissemination of existing eHealth technologies and practices in Europe and Latin
America tailored to local needs and standards, as well as to specific research actions that will lead to the development of scalable, flexible and usable
technologies.
• BSN
It is a portal for Body Sensor Networks(BSN) reseach. It provides links and information of BSN related topics, such as news on pervasive health
technologies, development platforms, conferences, tools, publications, algorithms, etc.
• AARP International
Entry point for the web resources of the American Association of Retired Persons, including their interactive world map of ageing related statistics, news
and events.
43.3 Blogs
International Association of Homes and Services for Ageing- Global Network Blog
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44 Journals and Books
44.1 Journals
• Psychology and Aging Published by the American Psychology Association
• Journal of Aging and Health "explores the complex and dynamic relationship between gerontology and health."
• Age and Ageing "is an international journal publishing refereed original articles and commissioned reviews on geriatric medicine and
gerontology. Its range includes research on ageing and clinical, epidemiological, and psychological aspects of later life."
• The Journal of Aging Studies "features scholarly papers offering new interpretations that challenge existing theory and empirical work."
• Open Longevity Science "is an Open Access online journal, which publishes research articles and letters in all areas of experimental and
clinical research in geriatric medicine and aging science."
• Abstracts in Social Gerontology "includes bibliographic records covering essential areas related to social gerontology, including the
psychology of aging, elder abuse, society and the elderly, and other key areas of relevance to the discipline."
• Ageline "produced by AARP, focuses exclusively on the population aged 50+ and issues of aging."
• Ageing and Society 'is an international, scholarly journal that aims to promote the understanding of human ageing, particularly from the social
and behavioural sciences and humanities."
• European Review of Aging and Physical Activity "offers the scientific community in-depth literature reviews from distinguished scholars,
meta-analytically based reviews, and introductory reviews for researchers and practitioners wishing to look beyond the borders of their
specialization."
• Journals of the Gerentological Society of America
• Journal of Applied Gerentology
44.2 Books
• Aging America and Transportation " This volume examines many of the issues faced by policymakers, transportation officials, vehicle
manufacturers, health and human services professionals, and aging adults themselves, as the largest generation prepares to drive into late
adulthood."
• Annual Review of Gerontology and Geriatrics, Volume 28, 2008 "This volume addresses the extraordinary need to educate personnel at all
levels in gerontology and geriatric medicine and in the design and delivery of health and social services."
• Older People at Home: Practical Issues "his practical and informative book focuses on several of the key social, cultural, and community
aspects of elderly care that GPs have identified as being problematic in their day to day work ."
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45 Research Groups and Consortia
104
46 Initiatives and Funding
This is currently a Linking Capsil. This type of CAPSIL is a resource to help create common point in the Wiki to enhahce the overall utilitiy of this Wiki.
As this page evolves we may extract Descriptive Capsils or refactor this page into smaller Linking Capsil entries.
Overall, this page represents an ongoing digest from the CAPSIL consortium members (and others) on the research, commerical initiatives and funding
related to ICT solutions related to the requirements for Independant Living. As a CAPSIL of knowledge this should act as an instructive entry point for a
range of people interested in who is engaged in independent living research, development and deployment. It can also help if you are looking to find
funding in this area.
46.1 International Initiatives in Aging
46.1.1 Global
• United Nations International Institute on Ageing
• International Federation on Ageing
• United Nations Program on Ageing
• Global Aging Initiative
• International Council on Medical & Care Compunetics
46.1.2 EU
• European Commission on Ageing Policy
• EU eInclusion: Ageing
• Global village
• Coordination Action AALIANCE - Similar to CAPSIL
46.1.3 UK
• BBSRC(Biotechnology and Biological Sciences Research Council) UK's leading funding agency for academic research and training in the
non-clinical life sciences
• Help the Aged, UK supports research into ageing.
46.1.4 USA
• US National Institute of Ageing
46.1.5 Australia
• Council on the Ageing (NSW) Australia
46.2 Interest Groups
46.2.1 Global
• International Network of Agencies for Health Technology Assessment
INAHTA's mission is to provide a forum for the identification and pursuit of interests common to health technology assessment agencies.
• International Association of Homes and Services for the Ageing
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46.2.2 EU
• Danish Centre for Health Technology Assessment (DACEHTA)
DACEHTA is a member of the INAHTA. The key aims of the Danish Centre for Health Technology Assessment (DACEHTA) include carrying out health
technology assessments (HTAs) with the aim of improving quality, standards and value for money. It is also an objective to integrate HTA-principles into
the running and planning of the public health service at all levels. The centre primarily targets health professionals and decision-makers at all levels as
well as related research communities.
• European network for Health Technology Assessment - EUnetHTA
European network for Health Technology Assessment, EUnetHTA, coordinates the efforts of 27 European countries including 25 Member States of the
European Union in evaluating health technology in Europe.
The general strategic objective of the Network: to connect public national/regional HTA agencies, research institutions and health ministries, enabling an
effective exchange of information and support to policy decisions by the Member States. During the first 3 years of existence (2006-2008) EUnetHTA
aims at developing an organisational framework for a sustainable European network for HTA along with practical tools to fill into this framework to
ensure timely and effective production, dissemination and transfer of HTA results into useful policy advice to the Member States and EU.
• Forisden // Medcom
MedCom is a co-operative venture between authorities, organisations and private firms linked to the Danish healthcare sector. In the 1999 financial
agreement between the counties and central government, it was decided that MedCom would be made permanent, with the following objective:
"MedCom will contribute to the development, testing, dissemination and quality assurance of electronic communication and information in the healthcare
sector with a view to supporting good patient progression".
• The Dutch National Knowledge Centre for Home Automation & Smart Living
The aim of Smart Homes is to improve the quality of life for everyone, young or old, with or without limitations. We work towards the development of
smart, understandable and accessible (mostly technological) solutions and services in the private home and everyday environment.
46.2.3 USA
46.2.4 Other
46.3 Funding
46.3.1 EU
46.3.2 USA
46.3.3 Other
46.4 Research Center or Projects
46.4.1 EU
• SOPRANO Service-oriented Programmable Smart Environments for Older Europeans
The SOPRANO project is to develop affordable, smart ICT-based assisted living services with interfaces which are easy to use for older people and
familiar in their home environment.
• Netcarity
Netcarity is a European project researching and testing technologies which will help older people to improve their wellbeing, independence, safety and
health at home.
• INHOME Project An Intelligent Interactive Services Environment for Assisted Living at Home
The goal of the INHOME project is to provide the means for improving the quality of life of elderly people at home, by developing generic technologies
for managing their domestic ambient environment, comprised of white goods, entertainment equipment and home automation systems with the aim to
increase their autonomy and safety. Contrary to the practises followed up to now, the INHOME project focuses on the problem of appliances flexible use
by discriminating between experienced and inexperienced rather than enabled or disabled users. By adopting this radically different standpoint the
project is posed to set out a generic set of appliance design guidelines targeting the intensification of home appliances use and user dependency.
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46.4.2 USA
• CREATE Center for Research and Education on Aging and Technology Enhancement
The center is gathering information on users needs and preferences; determining problems with existing systems; and, exploring the efficacy of potential
design solutions. A major goal is to insure that the results are disseminated to system designers and implemented in a wide variety of settings.
46.4.3 Other
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47 Other Websites and Blogs
• Smart Thinking is the home of Guy Dewsbury's thoughts on the use of technology to help impaired and disabled people; person-centred
design and the home.
• The Telecare Blog also run by Guy Dewsbury and dealing with person-centred Telecare.
• Clinfowiki "Clinical Informatics Wiki" The goal of this resource is to provide clinical informaticians around the world a place to document and
discuss many of the most important lessons they have learned in their day to day activities.
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48 Events
The Unit of the European Commission "ICT for Inclusion" organised a workshop on 27th January 2009 to promote call 3 in the CIP-ICT PSP
programme, and to get ideas and input on how to proceed from 2010 and beyond.
• Workshop on European activities on e-Inclusion, January 27th 2009, Brussels
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49 Tom's Story
49.1 Tom Capsil?s New Life
This document describes a brief story illustrating a vision of how technology can help, in the foreseeable future, to maintain independent, high-quality life
for elders. Some of the technologies incorporated in this story are already deployed in the field or in the laboratory, several are the focus advanced
research and others need to be investigated. The described environment is focused on unobtrusive sensing and assessment and on minimally obtrusive
interventions. The underlying algorithms incorporate the state of the art statistical pattern recognition and optimal decisions under uncertainty. The
reader may notice the unobtrusive and subtle way that the system adapts to the elder?s values and preferences. The economic feasibility of the
technology-based approach and clinical effectiveness require further research.
Download as Tom Scenario PDF
Details
Tom Capsil?s New Life Zooming along a familiar winding road on his way home from his Thursday consulting session, Tom Capsil turned off the
autopilot in his leased electric car ? enjoying the feeling of control. He likes to take over from the autopilot to keep up his driving skills ? his coach
encourages him to practice, as much as possible, without automation. Tom allows himself the luxury of the leased car since his 75th birthday while he is
still commuting to his part?time work ? he is actually enjoying helping a couple of young kids starting their own bakery; besides the money that helps he
loves the fact that he can make a difference. Several hundred meters after he turned of the highway, as clockwork, the traffic slowed down to a crawl at
a very familiar intersection; this is the place where his parents started their first business. As he often did, he began to reflect on his father?s life some
27 years ago when his Dad was exactly Tom?s age.
His Dad?s life after retirement was much harder than Tom?s; on top of his own struggles with arthritis, obesity, and diabetes, he had to worry about
Tom?s Mom who probably had Alzheimer?s disease ? at that time the only definite diagnosis was an autopsy. His Dad agonized about everything ?
from Mom wandering away or falling down in her bedroom, to whether she took all her medications and vitamins in a complicated, difficult to remember
regimen ? clinicians then had still exaggerated beliefs in the effectiveness of vitamins . In those days, Tom lived a couple of hours away from his
parents, with his own family and although he kept in touch by telephone as much as possible it was never enough. On the phone, his Dad would always
be upbeat and Tom was never sure what was actually going on. But now Tom?s thoughts were suddenly interrupted by a chime ? the automobile safety
system detected an attention lapse by his eye movement pattern and by EEG measurements using remote laser sensors. The chime came just in time ?
he almost hit a pedestrian crossing the street in the front of his own house. This does not happen frequently in the outskirts of the city.
The garage door as well as the front door opened as soon as the security system in his house detected the RFID signal transmitted by his watch. He
appreciated the welcoming whiff of balmy air assured by the remote climate control anticipating his arrival. Tom, supported by a smart cane, walked into
the kitchen later than usual and was greeted by the friendly voice of, Carobot, his robotic valet, gently reminding him of the session with his remote
coach. But Tom did not start his coaching session yet ? he was a little embarrassed since he did not yet do his daily exercise. Instead, using a voice
command, he started his exercise game routine. Being overweight most of his life, he had not been not much of a jock, but this game?based system
was actually fun! It was physically and mentally challenging, without embarrassment, within the privacy of his bedroom. He was totally amazed because
he was clearly improving ? imagine at his age. Today, he pushed himself particularly hard because he wanted surpass his previous record. He can push
himself hard because Tom is well aware that the system monitors his vital signs and does not let him overdo it. This close monitoring is particularly
important because of his congestive heart condition diagnosed a couple of years ago.
The results of his exercise were instantly communicated to his coach, and when Tom actually initiated the session there was already a message
praising him for his accomplishments. The coaching system had already incorporated today?s weight measurements (automatically assessed by the
load cells in the bed as well as a scale in the floor mat in of the bathroom), blood pressure measured by a sensor in his ring, and chemical analysis for
sodium ion concentration in his urine performed by the toilet. The coaching system, as well as his coach, were pleased with his outside activities,
socialization and diet. Even his balance had improved so much that his Carobot, a recent robotic addition to his house, just stands by when he gets up
at night to go to the bathroom rather than providing him with mobility support. Today is a very good day, and it will get even better; like a small child,
Tom can hardly wait for tonight.
Tom has some time to rest before he needs to get ready and so he sits down in his favorite chair. As frequently happens, his mind wanders, reminiscing
about the recent past. It wasn?t always this good. Only a year and a half ago he was in the middle of a deep depression: He was just diagnosed with
congestive heart failure, his best friend had died, and he had no one to turn to. Of course, his children Michael and Eva love him and would do anything,
but they have their own lives many kilometers away, and the last thing he would want to do is to be a burden to them. At his lowest point, a neighbor of
his told him about the integrated elder care system, Living Independently with Functional Technology (LIFT) that seems to be sweeping the world. Tom
is now quite aware that LIFT was the result of major research and development efforts supported jointly by the European Commission, the US
government, and private organizations.
At first Tom was resistant, he did not want to be monitored, but then what did he have to lose? And the rest was history ? he is a different man now with
a new outlook on life. His activities are continuously monitored, including his nutrition, bathing. When he gets up in the middle of the night to go to the
bathroom, the light turns on gradually and gently illuminates the bathroom and the robot stands by to give him a balance support ? only when he needs
it; the system has adapted to his habits and only responds when something out of ordinary happens. Even then, before an alert is sent out, Tom has the
ability to cancel it, if he is OK. Tom feels much safer than before and became much more active, although he allows only a small number of people to
have access to this information. In a funny way, this monitoring system brought him close to his son. Tom learned very quickly, that Michael actually
checks his status several times a day, even when he doesn?t call. The thought brings tears to Tom?s eyes.
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His thoughts are interrupted by a video?telephone call from his granddaughter. He marveled at the simple technology behind this marvel: his favorite
chair is wired to let Michael?s family know that Tom is sitting in his chair and may be available for a phone call. Tom breaks into tears when his
granddaughter proudly shows off her science project. She reminds him so much of his darling wife that passed away unexpectedly almost five years
ago.
Time flies and it is time to get ready for Tom?s Thursday night outing. About half a year ago, Tom, with a great deal of reluctance, joined this group of
film buffs; they usually get together once a week in a local pub, have dinner, show a film, and then they talk about the film. But what excites Tom tonight
is that is thoughts about Ana. She is a beautiful, younger woman in her early seventies that he noticed shortly after he joined the club. She makes the
most insightful comments, and shares with Tom his taste in films, politics and food. But he did not have the guts to talk to her, and Ana did not notice
him until recently, after he lost his first 15 kg and gained his old self?confidence and joy for life ? thanks to the vigilance of the coaching system. He
would have never believed that he could lose all this weight and build up his muscles and self?confidence at his age. For the last twenty years he felt
unattractive and never really had any social life ? especially after his wife passed away. Now he feels all nervous, like a high school kid, because tonight
he plans to ask Ana out.
Approaching the door with his coat on, the context?aware reminding system sounds a gentle alert reminding him to take his medication. Even though it
is 30 minutes before the medication is due, the system figured out that Tom is likely to leave his house without taking his medication on time. The film
tonight, ?When Harry Met Sally? was perfect setup for his mission. After a short debate about love, he got his courage to ask Ana out and she said
?yes.? They decided to meet in town next Saturday evening. Tom, back in his house, taking care of his teeth, lying in his bed as the LIFT system slowly
dims the light and lowers the intensity of the simulated distant sounds of an ocean. Tom Capsil, a happy man falls into a deep sleep, knowing that his
safety and well being is continuously assured by the always alert LIFT system.
49.2 Gaps
• Auto-pilots for motor vehicles are not commercially available.
• Attention-span devices (for cars for example) have been researched but are not commercially available.
• The personal assistant Carobot. Personal robots are not available for consumers.
• Physiological measurements are not currently used as input to coaching or cognitive monitoring devices. The two types of device operate as
seperate devices. They need to be brought together.
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50 Sean's Story
http://www.donegalcottageholidays.com/theoldfarmhouse/z-house-2.jpg
50.1 Morning
Sean awoke just after dawn as usual, just before his intelligent home monitor system triggered his wake up alarm went off and turned up the lights in his
bedroom. The small visual display beside the bed indicated that Sean had 7.5 hours of sleep with a sleep quality index of 75%. "Not too bad" he thinks.
Non contact sensors located under the mattress recorded motion, respiration and ECG data through out the night. As Sean had grown older a good
nights sleep had become a luxury. Sean like more than half of all adults over 65 have suffered from a sleep complaint. However some recent life style
changes suggested by his doctor seem to helping to improve his sleep quality.
Sean being a farmer all his life still has the motivation to get up early each morning. His sheep are reason to get up in the morning and a reason to live.
Although he makes very little money from farming it gives him a sense of worth and sense of independence. Sean picked up his smart phone, clips it to
his belt and inserted the discreet wireless earpiece and smiled to himself as the sound of birdsong filled his ears. Seans hearing had started to fade
some years ago (only natural in a man of 68 he tought) and the earpiece functioned as a hearing aid as well as delivering updates and information from
the 'lifestyle management assistant' software living on his smart phone.
Sean was used to solitude but he had come to think of the soft voice whispering reminders in his ear as a companion, he had even named her..'Sheila',
although he would never confess this to anyone else! Sean had developed a variety of personal and idiosyncratic ways for remembering crucial daily
tasks over the years which worked with varying degrees of success especially as he gotten older. 'Sheila' now provided him with a personal safety net
and he quickly grown to accept and rely on. 'Good morning Sean' the software said, 'don't forget your nicoteine patch!'. Sean had always said that
smoking was one of the few indulgences in his life but after some strong advice from his doctor he had started trying to quit. He picked up a patch from
the pack beside the bed and stuck it on.
Heading downstairs in his rural farmhouse, motion sensors pickup his movement and switch on the lights for him. 'Shiela' whispered in his ear again
"Remember to weigh yourself for the doctor' and he turned and went into the bathroom. Sean had recently been diagnosed with Diabetes, a result of his
other indulgence...alchohol, but was managing it with a program developed with the doctor. He stood on the weighing scales as he did each morning
Next his measures his blood glucose level with a digital glucometer. Both pieces of data are sent wirelessly to his health monitor device located in the
living room.
After this was done he made his way to the kitchen (by now it was bright and the home control system his younger sisters had insisted he install
switched the lights off) and began to make his breakfast. Sean had always believed in a good solid Irish fry-up to start his day but as his doctor had
pointed out this was contributing to his high cholesterol level and so he was making an effort to eat more healthily. He had a consultation with a
nutritionist and together they had devloped a healthy eating plan. Unfortunately he had run out of his usual cereal and was looking blankly into the fridge
searching for ideas (never having been a very adventurous cook), 'Need some help with breakfast?' asked 'Shiela'. 'Sure, any suggestions?' he
replied...he didn't mind talking to the PDA while on his own but when he was in public he switched over to the large touch display built into it. 'Shiela'
recommended some scrambled eggs and toast, and asked would he like to add cereal to the shopping list? Seans shopping was delivered once a week
by the local supermarket, based on the list 'Shiela' emailed them and maintained using information from the 'Content Aware' fridge and the list of
recommended foods from the nutritionist.
The smart phone bleeped to indicate a message had arrived whilst he was asleep. Peadar, his 64 year old brother, with whom he ran the farm was not
going to be there until lunchtime as he had an appointment. Sean made his way out to the workshop and strapped on his ExoLift hydraulic exoskeleton.
This was a recent purchase he had made with a grant aimed at maximising the farming workforce. When strapped on over his overalls it allowed him to
easily lift large and heavy objects..."just like being 18 again" as he thought to himself. The mini-tractor (another recent purchase) followed him out of the
shed and across the field as he went about his business.
50.2 Afternoon
The mornings work flew by and Peadar joined Sean at lunchtime. He'd brought takeout from a local fast food restaurant with him, Sean was sure his
nutritionist would disapprove. "Ah well" he thought, "you're only young once!" and tucked in. To be on the safe side he took a picture of the burger and
chips and added it to his food planner, let the nutritionist worry about balancing it later! While they were eating a call came in from their sister in America.
She had moved there many years ago but her and her family kept in regular contact with Sean and Peadar via phone and the internet. "Would you like
to transfer the call to the TV screen?" asked 'Shiela'. Sean tapped the smart to confirm, the TV switched on and Nora's face appeared in 3D on screen.
"Hi there big brothers! How's life on the farm?" asked Nora. "Ah, same as it ever was..." said Peadar. "How would you know?" said Sean, "Sure you've
hardly been here all day!" Nora laughed, "Well some things never change! We've booked our flights to come visit ye next month, I'm sending you the
details now", an incoming email notification popped up in the corner of the screen and Sean tapped on his smart phone to open it and display the flight
details alongside Noras face. "Ah that's all grand" he said and added the details into his calendar with another tap. Peadar and Nora chatted for a while
longer as Sean went to get another nicoteine patch after a prompt from 'Shiela'.
Sean and Peadar spent the rest of the afternoon in the fields until it got too dark to work. Farming had never been an easy life but with food prices so
high and the technological help of the ExoLift and SmarTractor, the rewards outweighed the toughness of the work. The usefulness of technology had
been brought home to Sean a couple of years ago, shortly after he'd got 'Shiela' when he's had an accident and hurt his leg when out in the fields. His
home control system detected an abnormal pattern when he didn't return for lunch. When he didn't respond to an alert request the home control system
notified Peadar (his nominated notificee). Peadar made his way to Sean by locating the GPS signal built into the smart phone and brought him to the
hospital. Thankfully he made a quick recovery but it really brought home how useful the technology could be and led him to invest in the ExoLift and
SmarTractor.
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50.3 Evening
Peadar made his way back home and Sean decided to start preparing his dinner. Not being much of a cook he found the microwavable ready meals
from the supermarket a godsend. 'Sheila' made a couple of suggestions based on the healthy eating plan (modified to take account of his early fast food
meal) and Sean sat down to dinner and a movie. As he was watching 'Sheila' popped up a reminder that he was due for a music jam session with some
of his friends. Sean had always had a love of traditional Irish music and was a keen tin-whistle player, he saved away the movie for later and joined the
jam. Although it was getting harder for him to get to the regular sessions in his local pub he had recently become a member of an online group that used
video and social networking to play together online. Sean saw the names and images of his regular session mates pop up on screen one by one and
began to tap his foot as the familiar strains of Mileys fiddle swirled through the room. Sean began to play along and his other online friends joined in one
by one. As the last strains of the reel they'd been playing died away Sean and the others began to chat and catch up on the local gossip. Sean lit up the
one cigarette a day he still allowed himself (after de-activating the houses smoke sensor...he wouldn't make that mistake twice!) and sipped on a wee
glass of whiskey. Not quite the same as being in the pub, but not far off he thought to himself. At least he could stay inside to smoke!
50.4 Key Themes
• Meaning and Identity in late life
• Waking and getting up
• Food and healthy Aging
• Memory and remembering
• Dignity and privacy
• Independance and social interaction
• Productive activities
• Activity ? Active Lives vs Succumbing to old age
• Family relationships
• Entertaining Self and Others Inside and Outside the Home/Social Connectedness
50.5 GAP Analysis
• Under mattress bed sensor which can accurately measure heart rate and respiration to enable sleep staging
• Intelligent digital avatar - 'Shelia'
• Virtual collaborative entertainment environments
• Interactive nutrition programs
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51 Anna's Story
Seven o?clock in the morning, as every day, Anna is going to have her breakfast. Anna is 79 years old, and since 6 years ago, when she underwent a
hip prosthetic, she usually tidy up the house before to perform the rehabilitation exercises. The rehabilitation is ended many time ago but, as suggested
by her doctor, she regularly goes to her physiotherapist office to preserve has much as possible her motion abilities. Anna suffers of a serious form of
arthrosis that is going to damage with a notable pain all her articulation, especially the hip and the hands. Even if her left hand is almost closed, it will not
be this problem to prevent her from living her life as she want, in fact Anna as all the women of her family, has a strong character and not admit to
change her habits so simply. Her two sons fear was related to the possibility that Anna could fall in her home without to be in position to call help. In
particular, in the last year, her healthy condition have an aggravation due to her osteoporosis became more aggressive augmenting the risk and the
consequences of a falling. These are the main reasons that inducted Anna to well accept to install in her home a monitoring system, especially when the
alternative is the hospitalization. The system she is using monitors her movement highlighting if there are motion anomalies, and provide him with a
support to remember both the medication at the correct time and to follow the diet.
In the kitchen there is one of the signs of the system presence. A double touch screen in the elegant shape of a book is placed closed to her sink, it
contains all her recipes included her famous Pasqualina pie. Her son Matteo has helped to put all data in this book. On the left screen, it appears the
suggested food for the day, associated with the time and the diet Anna has to follow to preserve the prosthetic. The suggested food will guarantee the
correct balance of calcium , sodium and sugar, between all the meals. This morning the magic book suggests a breakfast that she don?t want. With a
simple pressure of her finger on the screen, the book suggests an alternative food and she selects her preferences. After her breakfast, she takes her
prescriptions: the pills of the morning are on the open part of the dispenser, and she goes to her room for the exercises.
Today, differently from usual, she not goes to the physiotherapist office, but she will perform her exercises at home. At the 7:30 am, just after finished to
prepare the room for the activity, she receive the call from her physiotherapist, and his image appear on the projection screen beside the bed. After the
regards, the therapist starts indicating where Anna has to put the patched on her body. Such patchs are sensors that will allow the system to track
physiological data and to track the motion of the joints. Anna knows that, with the help of her system, she can perform autonomously the exercises, but
she prefer to work with the therapist, especially because he is a so courteous man and she loves to chat with him. It is not the first time that she needs
to change the time scheduled for the exercise. Sometimes the therapist isn?t at disposal for the time Anna required, just in that case Anna use the
automatic training, and she had not any problem to do it. However she prefers the contact with real human being and it is too old for change mind.
Today the reason for changing the schedule is very important, in fact she want to go as soon as possible to the market to buy the more fresh and best
quality ingredients for the dinner. This evening Anna will prepare her special dinner for her son Marco and his family. Marco work in north Europe where
lives with his wife and their young daughter, so Anna has not occasion to see them very frequently. Finished the exercises session, she perceives a
dizzy feeling because she stand up too fast. Immediately the system notices this condition: a sound feedback highlights an alarm situation and a voice
suggests to Anna to sit down again and to wait. After a minute the system tell her to stand up again. Following the indications written on the bedside
table display to check if everything is ok, Anna waits a little bit to stand before to go on her bathroom.
Before to go to the market, Anna goes to the kitchen to check on her Special Mobile Phone the shopping list and she notices a flashing hint indicating to
buy also the pressure pills. This remind has been automatically send on her Special Mobile Phone by the monitoring system. In fact when in the pills
dispenser the amount of pills are under a threshold, the dispenser gives an alarm to the System that communicates the remind to the appropriate
person, by the chose device. Since Anna can buy the prescriptions by herself she receive the remind on her mobile phone, in fact this is the device she
uses more frequently. Anna thought that it is good to notice it before to finish them, especially when she have already planned to go to out for buying.
Going back from the market Anna meet her neighbor Maria. Maria is widow too and frequently they meet each other for chat or to go to Mass. Since
purchase was too heavy for her, the shop assistant will provide it directly to her home, but not before the closing for lunch, so she has time for Maria and
decided to invite her to take a coffee.
Entering at home, Anna pushes a button just over the entryphone, and on the display appears the notice an adult host. Then she makes her way
towards the kitchen followed by Maria. Anna lights the flame under the moka and sits in front of Maria. When the coffee is ready she stands up to go to
the cooker and an acoustic alarm indicates a possible obstacle on the Anna?s way: on the floor there is the sweater of Maria, fell from the chair on
which she had placed. Since entering at home Anna indicates the presence of a host, some alarm are reduced and other increase they level of
attention, very powerful especially with her grandchild. Unfortunately Anna forgot to disconnect the audio feedback, and to switch the alarm on her
mobile phone vibration, so the alarm surprise Maria that did not remember the technological support of Anna particularly invisible for inexpert eyes.
Recovered the sweater, Anna rapidly serves the coffee in order to switch the Maria attentions, cause Anna do not like to receive too many questions on
her special support. While they are chatting Anna updates her magic book selecting what will be the menu for dinner in order to control the meal for the
lunch according to her diet. After one hour a providential ring at the door indicates both to Maria that is time to go and to Anna that her purchase is
arrived so she can have her lunch and above all she can start to cook for the dinner.
51.1 Gaps
• This story is quite good in that it illustrates what is very possible today, but not in practise being implemented! All of these scenarios
(medication tracking, video-conferencing, physiological measurements etc) are technically very possible at present however they are very
seldom linked together i.e. they exist as standalone solutions. For example it is quite possible to detect the 'fallen sweater' as an obstacle and
to raise an alarm, however the system would have to be set up to 'learn' this. The linking of such scenarios using 'todays' technologies is a
major gap that needs to be addressed.
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52 Mitsuko and Setsuko's Story
Setsuko pulled into her driveway at this moment. She had driven Mitsuko, her older sister, to the train station after visit. Even though Mitsuko still
remained very busy at 70 years of age, she always made time to visit the family home regularly. Setsuko, five years younger than her, had moved back
to the family home some years ago to closer to their mother when their father had passed away. Mitsuko wanted to bring her out to Tokyo, where the
sisters were living at the time, but she insisted she?d never feel comfortable being away from the home she?d loved so much.
Setsuko tapped the ?Park? button on the dashboard, which switched on the automated parking system. The system automatically aligned the car and
began the procedures to un-dock her smart wheelchair from the car. She could have steered herself, but she decided to write a quick email to Mitsuko,
and so she allowed the system to automatically steer her all the way inside her home. She was greeted by her support robot. She affectionately called
him Enjo-chan. Enjo-chan usually waited in his charging station in the kitchen, but when she pulled into the driveway, the system notified Enjo she was
home. Enjo told her there were two video notes on the system, and she asked Enjo to play them on the kitchen display. The first was her weekly
check-in from her doctor. He said her vitals looked good so he?d just leave a message. She thought she wouldn?t bother to call him back, as she was
feeling fine. The second message was from her daughter Rika, apologizing for not being able to come home along with her aunt Mitsuko. Setsuko told
Enjo she wanted to call her. Normally they would video chat, but she didn?t answer on her home phone and so Enjo automatically dialed her mobile
phone instead. They talked for a few moments about Mitsuko?s visit, and Rika mentioned she had received a message from Setsuko?s doctor saying
she hadn?t answered her weekly call. Setsuko assured her she was fine, and insisted that Rika go and visit Mitsuko soon, both living in Tokyo, which
she agreed was a good idea.
Mitsuko was dozing on her train ride home but was roused by her phone vibrating to remind her to get off at the next stop. As she exited the station,
there was another vibration as she turned right to go up the hill, instead of left, down towards her condominium. She wanted to stop by the market, so
she canceled the notification. At the market she saw some sweets that looked tasty, but she scanned them with her mobile phone first to be sure they
met her diet limitations. The system, which was tied into the main system running in her home, helped her manage her type 2 diabetes and hypertension
by not only checking her purchases for her, but also by monitoring her food intake at home.
This was all made possible because last year her condominium had been remodeled to incorporate the Japanese version of the Living Independently
with Functional Technology (LIFT) system. The new system added a vareity of beneficial features that made it possible for her to continue living
independently in Tokyo. As she entered her condominium, the system notified her she had a message. It was from her niece Rika, asking if she could
come over sometime during the week. She was always happy to have her niece come over. It had been a while, but she sometimes came with her
children, it was always fun to have them over. After sending a response, the main screen in her living room showed a notification that the system had
ordered the refill of her medications. She used the screen to start warming her bath and sat on the couch to watch the news while she waited.
Setsuko too was about to enter her bath. Enjo stood by waiting for her command. She asked "Would you help me in the tub Enjo-chan?", and Enjo
maneuvered himself into position to prop her up. She was always amazed at how his arms would move softly if she pushed them, but would immediately
stiffen if she started to fall. After a relaxing dip in the bath, Enjo helped her to the seat so that she could dry herself and then back into her chair. The
LIFT system calculated it would be best for her to sleep soon and, via Enjo, notified her as such. Setsuko decided to just grab the book Mitsuko had lent
her from the kitchen and then moved on to her bedroom. She didn't see Enjo around, perhaps he had gone to the kitchen for her medicines, and so she
attempted to climb into bed herself. However, she lost her grip as she was doing so and slipped to the floor. Before she even had time to realize she had
fallen, the pressure sensors arranged around her bed detected a possible fall. Immediately the system began a series of commands, starting with
summoning Enjo to the bedroom. The system also connected to the health support center and passed partial control of Enjo to the support center
staffer. As he entered the room not Enjo's voice, but that of the support center staffer, asked if she had fallen. Had she not answered, an ambulance
would have immediately been dispatched, but Setsuko called out she felt fine, she had simply slid to the floor. Enjo chimed in stating that the
post-analysis of the pressure sensor data showed she had not impacted with enough force to have injured herself. Setsuko restated she had simply slid
onto the floor and not fallen. The support center was contented after reconfirming the data from the system and seeing Setsuko moving normally via
Enjo's cameras. As a precaution her file was flagged so that her physician could follow-up on it the following day. After assurances she was fine, the
support center staff said goodnight and Enjo helped Setsuko up into the bed. Enjo asked she would like to call her daughter, but Setsuko insisted there
was no need. Enjo gave her her nightly medicines and went into standby mode while she read a bit. Once Enjo detected via the sensors in the bed that
she had fallen asleep, Enjo picked up the book and placed it on the nightstand before returning to his charging station in the kitchen.
The following morning, the system LIFT began the steps to awaken Mitsuko. After assessing traffic conditions, the system checked her sleep pattern via
the in-bed sensors. The system determined it was a good time to awaken her. She maintained a position as an adviser at the magazine where she had
always worked. She didn't have to go everyday, but she enjoyed working with the young people, a chance to mold the future of the business. She arose
gently to the sounds of her favorite singer. After using the restroom, she checked the results of her urine glucose. "Still doing fine." she thought. The
system also notified it her it would be a little chilly, and advised she dress warmly. There also seemed to be a notification that Setsuko had had a minor
fall. She asked the system to connect her. Likewise, Setsuko was rising as well, and by this time Enjo was already helping her clear the table. Her
cellphone in the living room started to ring, and Enjo offered to connect via his display. Mitsuko's face popped up on Enjo's chest. Setsuko assured her
she had just had a minor slip and was fine. Her doctor had even made a follow-up call that morning, and said she should be fine. They spoke for a bit
about Rika visiting soon, but Setsuko had to cut it short because she had a painting class at the community center. They said their goodbyes and Enjo
finished clearing the table while Setsuko made her way toward the door.
At work, Mitsuko was asked to review the proposals for a section of the web-based section of the magazine that featured young aspiring photographers.
Working with her was a younger lady that had worked under Mitsuko when she was a section chief. They decided to have lunch together as well, as
they had not seen one-another some time. Just before lunch, Mitsuko checked her glucose levels using the sensor on her cellphone. Her former
subordinate was amazed, cellphones really could do anything these days she said. The results were good and the system automatically updated the
main system back at her condominium. After lunch, Mitsuko decided to do a little light shoppping before heading home. She stopped by a cake shop to
pick up a little something for Rika's visit. Setsuko was just packing up her painting materials when her cellphone rang. Mitsuko had sent her photos of
two cakes, asking which Rika would like better. Setsuko joked to her friends in the painting class, her sister who had such a great head for the magazine
business couldn't pick a cake. After suggesting the second cake, Setsuko and her friends made their way to the park next to community center to eat the
boxed lunches they had brought.
Later that evening, Rika got two emails confirming both her mother and aunt had reached home safely. She didn't think it was necessary to check on
them constantly, but it was nice to be able to find them when needed. Like the time her mother's car got a flat tire, she was able to give the roadside
service staff her GPS information. Or the time Mitsuko called her out of the blue to meet for lunch. Following the GPD signal of her cellphone, she found
her so quickly. And tonight, knowing her aunt had arrived home, she could head out the door to go visit her.
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52.1 Gaps
• Automobile auto-pilots or 'park' systems are not commercially available although much research has been performed.
• Enjo-Chan - Personal care robots are still within the domain of research and are not available to general consumers. Companies like Willow
Garage [1] are developing open source robots that they hope eventually can be deployed to individual consumers. Also prominent in teh
research sphere is Stanford's STAIR robot [2].
• The 'fall' that Setsuko experienced can technically be picked up by modern pressure sensors and there are commercial devices available (See
CAPSIL on Home and Mobile Systems). However the real valuable piece of information here is why Setsuko fell. In the spirit of true proactive
healthcare, there should be a full root cause/corrective action investigation perfromed here to ensure it doesnt happen again.
• There are many systems here such as LIFT, traffic analysis, GPS and medication tracker devices that in their own right are all available at the
moment. The gap though is in the linking of these units together to form an overall system. Various CAPSILs have refered to the problem of
Information Fusion (Home and Mobile Monitoring Systems and Wireless Body Sesnor Networks). Progress is needed on standards,
interoperability and information fusion techniques so that currently available technologies and solutions that can be deployed in standalone
mode, can be interconnected to form a truly 'proactive' healthcare system.
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53 Jackie's Story
Tonight is bingo night for Jackie, and Jackie loves bingo night! A great chance to catch up with the girls, not to mention the roll she was on last week!
Although she plays with her friends remotely during the week using the online bingo forum, it is not the same as being out and in their company. Jackie
felt a soft nudge and looking up from her book saw CLARC?s blue eyes shining at her. CLARC (Care and Living Assistive Robotic Companion) tilled her
head pointedly looking at the mobile medical unit on the tray it was carrying. Jackie smiled and sighed placing her finger on the unit?s sensor pad, while
it checked her blood glucose. CLARC?s eyes changed to green, the all clear. Jackie picked up her book again, but a soft chime interrupted. CLARC?s
eyes, blue again, were indicating towards the small pile of pills that had dropped onto a plate while a glass of cool water was being poured. Jackie didn?t
know what she would do without CLARC to remind her to monitor her blood glucose and take her medicine. All those pills, it used to be so confusing to
remember what to take, how much, and when. Jackie was lucky that she could now control her type 2 diabetes through oral pills, diet, and exercise.
A low whistle came from CLARC after she had taken her pills, and she threw up her robot arms and wheeled into the bedroom. Jackie giggled as she
got up and followed where she changed into her workout gear. CLARC always got so excited when it was time to exercise. The snugly fitting t-shirt and
leggings were easy to put on and comfortable, allowing her to move freely. You would never guess that there was soft fabric sensors integrated into
them which could follow her movements, recreating them on the wall screen.
Jackie had moved to Bexhill-On-Sea after Peter had died. The house in Glasgow just felt too big and empty. Jackie had been obese for a long time, but
gradually it had been slowing her down till she very rarely left the house. Although she could order shopping online, it was the human contact she
missed the most. Her daughter and her family lived in Hastings and with Peter gone it had felt like the right time to make a move. Sarah, Jackie?s
daughter, had managed to find her a lovely self contained flat on the sea front and was only a 30 minute drive away by car and so was able to visit
Jackie several times a week.
A simple command began the exercise program; CLARC projected the routine onto the wall and played music through speakers. The sensors in the
garments wirelessly transmitted the data to CLARC where they could be interpreted and mirrored on the projection of the exercises. To begin with a
game was played to warm up her joints and muscles. Jackie reached up to touch the shapes as they appeared before they disappeared again. Feeling
nicely warm Jackie took her Pilates band and following CLARC?s instruction worked on her muscle toning exercises. After, stretching out, Jackie
contemplated her life before she had moved here. Already she had lost 2 stone in weight and had found both her balance and stamina improved. She
had seldom left the house before, partly because she feared that she would fall and partly because the effort always left her feeling so exhausted.
CLARC presented Jackie with the vital stats from her workout, adding them to her workout log and sent them to Jackie?s doctor. No abnormalities had
been detected such as an over elevated heart rate.
CLARC started chirping and moved into a position in front of Jackie. It was Sarah calling on the video phone. Even with CLARC there to keep an eye on
her, Sarah still liked to keep a close eye on Jackie, it was sweet to know that she cares so much, even though Jackie had tried to reassure her that help
could get to her in minutes should anything happen to her. Just last month Jackie had slipped coming out of the bath and had bumped her hip on the
sink. Luckily, nothing had been broken and her hip had only been bruised. CLARC had been on stand by when the pressure sensors in the bathroom
floor had been activated indicating a sudden sharp impact. CLARC had entered the bathroom where Jackie told it she was conscious but would require
help as she could not get up and had hurt her hip. CLARC had immediately notified the emergency services and stayed with Jackie playing a game to
ensure she was fully coherent till they arrived. After this fall Jackie had been put on the waiting list for an up graded CLARC that could also assist her to
stand if she fell and did not need immediate medical assistance. Gradually the NHS was being able build an infrastructure to support the rising demand
for independent living in the UK, however, it was not widely available through out the whole of the UK yet as the pilot trials had begun in the South of
England, but slowly the technology was being made far more available.
After chatting to Sarah and arranging to meet the next day, Jackie asked CLARC to help her with her shopping list. To help her loose a little weight and
manage her diabetes CLARC kept a record of what Jackie ate and provided suggestions for meals that would be tasty yet healthy. Jackie enjoyed the
food that she was eating, although it had taken a little while to get used to, and it had been years since she had had so much energy.
A CLARC?s eyes lit up in anticipation, it was time for bingo! Jackie pulled on her coat and made her way to the front door. CLARC followed her then
stopping by the door whistled her a good evening with the girls.
53.1 Gaps
• CLARC - The Care and Living Assistive Robotic Companion. Such personal robotic companions are not commercially available at this time.
• Many of the functions perfromed by CLARC are available in a standalone capacity. For example glucose or other physical measurements,
medication compliance programs and videoconferencing. However these devices dont come in an 'overall package' but in standalone
solutions that need to be interconnected (by someone) to support a typical daily lifestyle routine. More work is needed putting together such
'overall packages' with technology that is currently available and can be linked together.
• Body Sensors woven in to common items such as garments and furniture are being heavily researched at this time and will no doubt appear in
commercial format in the coming 3-5 years
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54 Gap Analysis
These are our initial findings from our analysis of the current state-of-the-art in Gerentechnology research:
54.1 Gaps in Research
• Very little funding and emphasis on technology for transportation
• Integrated data storage and mining is not well represented
• Very little funding and emphasis on the sensor to social support (carer, physician, friend) middleware including data reduction and distributed
information infrastructure (false positives)
• Context awareness including emotional state, physiological state, and location needs more investigation in the home
• No commonality in definitions of ADL and AAL
• Very little funding and emphasis on interaction design for software across the user spectrum
• Programs for technology and life-coach cooperation seem to have no funding source
• Social isolation seems to be the least focused area
• Large scale (N>20) deployments are limited.
• Large scale deployments (>20) across multiple countries are limited
• Deployments across multiple regions (US, EU, Japan) are non-existent?
• Studies combining commercial-off-the-shelf (COTS) equipment (Tunstell, I-Plus, etc.) with novel middleware and interaction software is limited
• Standards are a key issue
• Where do people get information?
• Coordination of research outcomes with stakeholders/payers
• Lack of theoretical framework/basis for AAL research
54.2 Gaps & Possibilities - Initial Strategy
• Deploy web-based large scale survey on technology for independent living across Japan, EU, and US
• Technologies used
• Technologies needed
• Funding sources
• Distribute results on WiKi
• Conduct focused interviews with representatives from key consortia and funding bodies
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55 Aarons Sandbox
Here is an interwiki link Social Networks - test....
This is what you type [[w:Social Networks|Social Networks]]
or
Here is an acronym for Body Mass Index (BMI)
This is what you type [[wikipedia:Body Mass Index|Body Mass Index]] ([[acronym:BMI|BMI]])
(w, wikipedia and acronym are all keywords here for interwiki links. You can find a list of such keywoards to other wikis or other, online. If the wiki list
doesn't exist let us in UCD know and we can get the SQL update made.
Aaron uses Mash Logic in his web browser to enhance the web pages he views. MashLogic is a Firefox extension that works quietly under the hood to
enrich web pages with the kind of information and media you care about! It's a useful way to view pages on the CAPSIL wiki as you will automatically
see which terms are defined as wiki pages in Wikipedia and hence you can interlink to.
This is how you make a wikitable using the correct Table markup
Name
Effect
Poké Ball Regular Poké Ball
Great
Better than a Poké Ball
Ball
Test whether Wiki is OK after server upgrade
Games Found In
All Versions
All Versions
119
56 Chip? Antenna
The latest entry into the antenna field is the tiny ?chip? antenna. They are surface mount devices that are typically 8 by 5 by 2.5 mm, making them the
smallest design available. They may be found for frequencies less than 300 MHz and up to 2500 MHz. These antennas are similar to whips in behavior,
only much smaller. If an antenna can be reduced in size, while maintaining efficiency, bandwidth will be reduced. So these devices have a very narrow
bandwidth and must be made to the exact frequency.
These devices are very groundplane dependant. As a result, they are easily detuned by hand effects, the wrong size groundplane, or even the wrong
thickness and dielectric of the board. The chip antenna must be used according to the manufacturer?s recommendations.
For 433.9 MHz, we mounted a chip on a 5 inch long board and obtained a maximum gain of -10 dBd. Not bad when you consider that the spiral has
equal gain, but consumes five times as much area on the board. The 916 MHz version did better with a 2.6 inch long groundplane for a maximum gain
of -3.2 dBd. The polarization is parallel to the long axis of the chip, so maximum radiation is perpendicular to the long axis. There is a deep null (nearly
40 dB!) looking at each end of the chip. This would be a big problem if an omnidirectional pattern is required from a horizontal circuit board. When the
board is vertical, the pattern is omnidirectional.
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57 Accsense
57.1 Accsense
The Accsense modules are mainly produced for industrial environment monitoring such as medical refridgerators or laboratory animal research facilities
measuring qualities such as internal temperatures, humidity and air quality. The full system consists of a sensor/repeater pod to take the physical
measurements, a sensor/repeater pod for extending the range and a gateway pod which talks to the server. The server in turn, stores and graphs the
data and sends wireless/email notifications as to the status of the current environment being monitored.
http://www.accsense.com/images/products_header.jpg
57.2 Hardware Specifications (A1-01 General Purpose Pod)
• Ambient Temperature
• Vibration
• Humidity
• Acoustic Level
• Ambient Light
Sensing:
Ports available for External Sensing:
• 4-20ma Current Input w/ +20V Loop
• Digital Input (Contact Closure)
• Counter
• 0-5V Analog Input w/ +5V Sensor Drive
I/O:
Radios:
CPU:
IEEE 803.15.4 Transceiver
Unknown
Storage: Each pod can store up to 255 data points in the event no gateway can be reached.
57.3 Applications
• Hospital Monitoring
• Blood Bank Monitoring
• Pharmaceutical Monitoring
• Research Lab Monitoring
• Clinical Lab Monitoring
57.4 Power
The General purpose pod runs on 3 'AA' batteries providing a lifespan of 6 months using a 5min sampling rate.
57.5 Software
No software needs to be installed at the customers site in order to use Accsense. The user interface and commands for the Accsense modules are web
based.
57.6 Additional Information
• 3min youtube video on how easy it is to install Accsense A2 LAN-Wired Monitor
• Accsense Homepage
• SEQUOIA Technology Ltd.
Back to Sensors
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58 Activities of daily living
1.1 Background on ADL?s
The Medicare and Medicaid define activities of daily living as the essential elements of self-care. Inability to independently perform even one activity
may indicate a need for supportive services. Further instrumental activities of daily living are associated with independent living in the community and
provide a basis for considering the type of services necessary in maintaining independence.
Activities of daily living impairment are considered by the medical community to be a strong predictor of hospital outcomes (functional decline, length of
stay, institutionalization, and death) than admitting diagnoses. ADL impairment is also a risk factor for nursing home placement, emergency room visits,
and death among community-dwelling adults. Approximately 25% to 35% of older patients admitted to the hospital for treatment of acute medical illness
lose independence in one or more ADL. Risk factors for loss of independence in ADLs during hospitalization include advanced age, cognitive
impairment, and IADL impairments at admission.
According to the University of Michigan Medical School, ?functional impairment is defined as difficulty performing, or requiring the assistance of another
person to perform, one or more of the following Activities of Daily Living (ADL):?
122
59 ALARM-Net
ALARM-Net (30) - "Assisted-Living and Residential Monitoring Network for pervasive, adaptive healthcare" is a MicaZ-on-Tiny OS based wireless
sensor network for assisted-living and residential monitoring. It was developed at the University of Virginia by Stankovich et al. It integrates
environmental and physiological sensors in a scalable, heterogeneous architecture. The syatem features context-aware power management, dynamic
privacy policies, and data association. Communication is secured end-to-end to protect sensitive medical and operational information.
University of Virginias ALARM-NET System architecture and components
Similarly to the systems described above, ALARM-NET monitors environmental and physiological data of individuals in their residences, with focus on
the assisted-living and medical domains. Unlike other systems, ALARM-NET incorporates a Circadian Activity Rhythm (CAR) (which is 24 hour routine
of activity of a person) nalysis module that learns the patterns of daily life of the individuals, and influences the system and network protocols for power
management and privacy. For example, the dynamic privacy configuration rules change on the fly when an individual exhibits a behavior that is critical to
his health and enable the authorized medical personnel to access vital data, which is otherwise hidden or available for anonymous statistical purposes
only. Additionally, CAR enables advanced power management by anticipating which sensors should be kept active and which can be temporarily
disabled in order to conserve power according to the habits of the individual. A very neat aspect is the querying feature which allows appropriate people
(doctors, nurses etc) to access the information subject to enforced privacy rules.
59.1 Privacy
The Privacy Manager resides in the AlarmGate application and has three main functional components: the Context Manager, the Request Authorizer,
and the Auditor.
Context Manager: Collects and maintains the context objects about users and the environment from different analysis modules in the system. Request
Authorizer: Queries received at the Query Manager are forwarded to the Request Authorizer which makes access decisions by consulting the system's
privacy policies and context objects of the query subject. After each access request is decided at the Request Authorizer, it is recorded by the Auditor
module. Auditor: Maintains a trace of access requests in an audit trail, including the authorization decision made for each request (granted or denied).
University of Virginias ALARM-NET Privacy Management Module
123
59.2 Security
Access to an AlarmGate by user interfaces is limited to legitimate users of the system, who must authenticate themselves before being allowed to
continue. Queries for sensor data are authorized according to administratively configured privacy policies and context. After a client connects and
authenticates, communication between it and the AlarmGate on the IP network is encrypted whenever sensor data is reported. Messages sent and
received to/from the WSN by the AlarmGate must also be secured, using message authentication codes (MACs) and encryption and privacy policies
and context. Finally, the connection to the back-end database must also be resistant to attacks, a common requirement and one that is addressed by
commercially available programs.
The actual sensor network security is provided by SecureComm, a link-layer security suite was developed at University of Virginia for MICAz and Telos
motes and employs AES type encryption.
Back to Design Aspects of Body Sensor Networks
124
60 Algorithms
With the rapid improvement in hardware and software capabilities of wearable and ambient sensors, the challenge moved from that of obtaining sensor
data to that of analysing large quantities of data and providing a context aware autonomous system, leading to truly 'personalised' health care. Thus, this
section will focus on the common data analysis algorithms for pervasive healthcare systems:
60.1 On-node Data Processing
This refers to algorithms that can be implemented directly on a sensor node, allowing the optimisation of resources in terms of energy and
communications. These techniques are also essential if abnormal events were to be detected on the sensor node, and alerts were to be generated at
that level.
60.2 Sensor Fusion
The use of multiple sensors with information fusion has the several main advantages compared to single sensor systems. These include improved
signal to noise ratios, enhanced robustness and reliability in the event of sensor failure, integration of independent features and prior knowledge,
reducing uncertainty and improved resolution, precision, confidence and hypothesis discrimination.
60.3 Context Aware and Autonomic Sensing
The contextual information in Body Sensor Networks is mainly focused on the user's activity, physiological status and the surrounding physical
environment. Understanding the context in which the user performs his/her activities is essential in comprehending the activities themselves and their
relationship to prior and future activities, as well as environmental changes. Context aware sensing and autonomic sensing are quite linked. The latter
referring to networks that can autonomically configure, optimise, manage, heal, protect, adapt, scale and integrate.
60.4 Data Mining and Trend Analysis
With large amounts of data typically obtained from Body Sensor Networks, efficient data-mining is essential to allow important patterns to be recognised,
errors in the data highlighted and trends to be noted. This section will cover approaches that have been successfully used to provide pattern recognition
in Body Sensor Networks.
60.5 Falls Detection Algorithms
A variety of data analysis techniques incorporating value algorithms have been applied to Falls detection domain. Many of the approaches focus on
improving the selectivity of Falls. The issue of false positives is the most significant issues limiting the reliability of body worn Falls detectors. Many
efforts have focused on improving the classification base solely on the sensor by using a variety of mathematical techniques such as thresholding using
support data with sensor data typically accelerometer based to achieve greater selectivity. Alternatively supporting data sources have been utilized to
improve selectivity such as the inclusion of sound as additional source to a classifier to improve the accuracy of the falls detection.
125
61 ANT
61.1 Ant
ANT? is a proven protocol and silicon solution for ultra-low power practical wireless networking applications. Designed for 2.4 GHz operation, ANT is
perfectly suited for any kind of low data rate sensor network topologies in personal area networks (PANs) and practical wireless sensor networks (WSN).
ANT has been intentionally engineered to simplify practical wireless network development and optimize network efficiency. ANT-powered network nodes
can operate for years on a coin cell battery compared to months for other technologies. Rich in capabilities, ANT provides reliable data communications,
flexible and adaptive network operation and cross-talk immunity.
http://farm1.static.flickr.com/111/293388921_ac0ef85d12.jpg
61.2 Hardware Specifications
Sensing: User Defined
I/O:
Radios:
CPU:
• AT3 modules - 8 data channels
• nRF24AP1 (Nordic Semiconductor) with embedded ANT
protocol
• ANT11TR21I RF transceiver chipset (2.4GHz) with ANT
protocol
• ANT11TR41I RF transceiver chipset (2.4GHz) with ANT
protocol
• ANT MCU
Storage:
61.3 Applications
• Sports and fitness applications
• Garmin Edge 705
• Beurer WM80 Weight Management System
• Suunto t6 Heart Rate Monitor
• Concept 2 Indoor Rower
• Home and Inductrial Automation
61.4 Power
ANT is powered by small CR2032 coin cell batteries and can operate for months or even years based on the peak and average power consumption
being used.
61.5 Software
• Ant Website http://www.thisisant.com/
• NU Horizons, Electronics
• Ant Development kit from Nordic Semiconductor
• ANT(network) from Wikipedia
61.7 Papers
• Hajdu, J;"Provided Services of Social Networks for Sport", Seminar on Internetworking, April 2008.
126
Back to Sensors
127
62 Asbestos
Asbestos was extensively used as a building material from the 1950s through to the mid-1980s. It was used for a variety of purposes and was ideal for
fireproofing and insulation. Any building built before 2000 (houses, factories, offices, schools, hospitals etc) can contain asbestos. When asbestos fibres
are inhaled they can cause serious diseases which according to the UK Health and Safety Executive (1) "are responsible for around 4000 deaths a year
in the UK alone". There are three main diseases caused by asbestos: mesothelioma (which is always fatal), lung cancer (almost always fatal) and
asbestosis (not always fatal, but it can be very debilitating).
62.1 Asbestos Policy in the UK
In the UK Asbestos Licensing is on a "permissioning regime". The Heath and Safety Commission (HSC) have issued a policy statement "Our approach
to permissioning regimes" (2). Asbestos is classified as a category 1 carcinogen, with asbestos related disease causing over 4000 deaths every year in
the UK. Work with asbestos requires a high degree of regulatory control and the purpose of licensing is to achieve this. Licensing is an addition to the
general framework of health and safety law. It builds on the fact that the legal duty to manage risk lies with the organisations that create them.
The Control of Asbestos Regulations 2006 (3) came into force in November 2006. These Regulations bring together the three previous sets of
Regulations covering the prohibition of asbestos, the control of asbestos at work and asbestos licensing. The Regulations prohibit the importation,
supply and use of all forms of asbestos. They continue the ban introduced for blue and brown asbestos in 1985 and for white asbestos in 1999. They
also continue to ban the second-hand use of asbestos products such as asbestos cement sheets and asbestos boards and tiles; including panels which
have been covered with paint or textured plaster containing asbestos.
128
63 References
• 1. http://www.hse.gov.uk/lau/lacs/5-19.htm
• 2. http://www.hse.gov.uk/enforce/permissioning.pdf
• 3. http://www.opsi.gov.uk/si/si2006/20062739.htm
129
64 Atlas
64.1 Atlas
The Atlas Sensor platform consists of three layers that may be user specified depending on the application. The makeup of the layers consist of a
dedicated communcications layer, a processing layer and an interface layer for connecting additional devices.
https://www.store.pervasa.com/catalog/images/atlas3.jpg
With the use of the interface layer, the Atlas platform can connect up to:
Sensing:
• 32 Analog sensors
• 16 Digital contact sensors
• 6 RC compatible servos
The Atlas interface layer provides up to:
I/O:
• 8 Analog inputs
• 32 general purpose digital I/O
There is a choice of radio depending on the application:
• 802.11b/g based on the DPAC WLNB-AN-DP101 Airborne Wireless LAN Module
• ZigBee based on the Chipcon CC2420
Radios:
A universal patch antenna is also supplied as an additional layer with each radio.
Amtel Atmega 128L
CPU:
• 128K Flash
• 4K SRAM
• 4K EEPROM
• up to 16 MIPS
Storage:
64.3 Applications
Some typical Atlas applications include:
• Pervasive Computing
• Civil Structure Health Monitoring
• Remote Sensing and Data Acquisition
• Smart Homes and Smart Spaces
• Industrial Monitoring and Control
• Machine to Machine Communication (M2M)
• Fleet and Asset Tracking
• Healthcare
• Homeland Security
• Robotics
64.4 Power
The platform processing module contains data filtering and aggregation algorithims that minimize network traffic and increase the lifespan of the battery
powered nodes. Nodes also contain an external power connecter and an extra connecter to daisy chain to another node allowing for a large wired
network to run without taking up all the power outlets.
64.5 Software
Atlas is programmed via proprietry software - Atlasoft
• Pervasa - Commercial outlet for Atlas
• Mobile and Pervasive computing research
130
64.7 Papers
• King, J.; Bose, R.; Hen-I Yang; Pickles, S.; Helal, A., Atlas: A Service-Oriented Sensor Platform: Hardware and Middleware to Enable
Programmable Pervasive Spaces, Local Computer Networks, Proceedings 2006 31st IEEE Conference on 14-16 Nov. 2006 pp630 - 638
• A. Helal, S. Lim, R. Bose, H. Yang, H. Kim, and Y. Cho, Experience of Enhancing the Space Sensing of Networked Robots Using Atlas
Service-Oriented Architecture, In Proceedings of the 8th Asia-Pacific Conference on Computer Human Interaction (APCHI 2008). Seoul,
Korea, July 6-9, 2008.
• H. Kim, H. Yang, R. Bose and A. Helal, Enhancing the Sentience of URC using Atlas Service-Oriented Architecture, Proceedings of 8th
International Workshop on Human-friendly Welfare Robotic Systems (HWRS 2007), Korea, October 21-23, 2007.
• A. Helal, H. Yang, J. King and R. Bose, Atlas - Architecture for Sensor Network Based Intelligent Environments, Submitted to the ACM
Transactions on Sensor Networks. Submitted April 2007.
Back to Sensors
131
65 Automatic wearable fall detectors
Automatic wearable fall detectors which do not require human intervention are available in the wearable formats. Most of the automatic fall detectors are
designed to detect impact on the floor usually indicated by a sudden downward acceleration towards the floor followed by a horizontal body
Accelerometers are typically used to detect an impact in combination with tilt sensors and/or gyroscopes to determine the orientation of the faller after
the impact and tilt transitions during the event of fall. The selectivity of the devices are improved by the application of various falls algorithms There are
many designs available under this category depending on the locations for wearing the fall detector. The common locations for wearing fall detectors
include chest (neck pendant), hips or waist (belt or pocket), wrist or forearm (bracelet or watchstrap), knee or thigh (strap or stocking).
The chest wearable fall detector currently being developed at Seoul National University aims to accomplish real-time ambulatory monitoring of falls in fall
prone populations, including older adults. Researchers at the Swiss Federal Institute of Technology developed Speedy, a first prototype of a fall detector
integrated into a wrist watch. Conspicuous wearable fall detectors, may be perceived by the users as a stigma labeling them as fallers among their
peers.
65.1 Characteristics
Worn devices generally have the advantage of being small, light-weight, easy to- use, relatively low cost, and of being available on a ?plug and play?
basis for anyone who has a community alarm telephone. However, they can only perform their function correctly and reliably if they are worn correctly at
all times when the subject is up and about.
65.2 Commerical Solutions
• Task
• West Communication Ltd
• Tunstall
65.3 References
1. M N Nyan, Francis E. H. TAY; M.Manimaran and K. H. W. Seah, Garment-based detection of falls and activities of daily living using 3-axis
MEMS accelerometer, Journal of Physics: Conference Series 34 (2006) 1059?1067
2. Michael R. Narayanan, Steven R. Lord, Marc M. Budge, Branko G. Celler, Nigel H. Lovel,l Falls Management: Detection and Prevention,
using a Waist mounted Triaxial Accelerometer, 2007,Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité
Internationale, Lyon, France, August 23-26, 2007
3. G. Daniele, "Investigation of fall-risk using a wearable device with accelerometers and rate gyroscopes," Physiological Measurement, vol. 27,
pp. 1081, 2006.
4. R. E. Mayagoitia, J. C. Lotters, P. H. Veltink, and H. Hermens, "Standing balance evaluation using a triaxial accelerometer," Gait and Posture,
vol. 16, pp. 55-59, 2002.
5. H. B. Menz, S. R. Lord, and R. C. Fitzpatrick, "Acceleration Patterns of the Head and Pelvis When Walking Are Associated With Risk of
Falling in Community-Dwelling Older People," J Gerontol A Biol Sci Med Sci, vol.58, pp. M446-452, 2003.
132
66 Berg Balance Scale (BBS)
The Berg Balance Scale (BBS) consists 14 items that are scored on a scale of 0 to 4. Items include mobility tasks such as transfers, standing
unsupported, turning 360 degrees etc. The literature points to conflicting reports on the usefulness of BBS as a predictor of falls. Overall BBS is
described as having moderate-good specificity but low sensitivity in predicting falls.
66.1 References
1. L. D. Bogle Thorbahn, R. A Newton, Use of the Berg Balance Test to Predict Falls in Elderly Persons, Physical Therapy, Vol. 76, No. 6, 1996,
pp 576-583.
133
67 Biocompatability
The interface of the sensor node to the human is all important in body sensing networks. The material from which the sensor is made must be capable
of performing the sensing robustly while at the same time causing no side effect problems to the human interface (i.e skin or other surface. Examples of
such problems could be itching or rash or possibly even cell tissue damage. We are only interested here in sesnors worn outside of the body and not on
implantable devices (in-vivo) so we have restricted our research thus. There is a lot of material that discusses the science of biocompatible materials
and treats the various chemical/physical processes, however there is not a lot of material that deals explicitly with biocompatability and presents
demonstrable solutions.
134
68 Biotex Project
The BIOTEX project is an FP6-Joint Call IST-NMP (European call identifier: FP6 2004-IST-NMP-2 ) aims at developing dedicated biochemical-sensing
techniques compatible with integration into textile. This goal allows the monitoring of body fluids via sensors distributed on a textile substrate and
performing biochemical measurements. BIOTEX is addressing the sensing part and its electrical or optical connection to a signal processor. The Biotex
project was concerned with developing sensing patches, adapted to different targeted body fluids and biological species to be monitored, where the
textile itself is the sensor. The extension to whole garment and the integration with physiological monitors is part of the roadmap of the consortium.
These are currently being tested.
Biotex Fluid collection patch sensor
135
69 Books
69.1 Bulusu, Nirupama and Jha, Sanjay. WIRELESS SENSOR NETWORKS: A Systems
Perspective, Artech House, Norwood, MA, August 2005.
From the Publisher:
"This first-of-its-kind resource offers an in-depth understanding of wireless sensor networks from a systems perspective. The book describes and
categorizes the technological trends, leading applications, state-of-the-art platform developments, future trends, and challenges of sensor networks."
This practical reference also addresses middleware issues for sensor network applications and focuses on important application domains, showing how
specific applications influence the architectural design of networked systems. Contributions from leading international researchers and nearly 70
illustrations support key topics throughout the book.
© 2005 ARTECH HOUSE, INC.
69.2 Zhao, F. and Guibas, L. Wireless Sensor Networks: An Information Processing Approach,
Morgan Kaufman, 2004
From the Publisher:
?Wireless sensor and actuator nets, also known as motes and smart dust, arean emerging computer class based on a new platform, networking
structure, and interface that enable novel, low cost, high volume, applications. This text and reference is a critical link to create this new class by
covering the field of study for both practitioners and researchers.? ?Gordon Bell, Senior Researcher, Microsoft Corporation
?This book provides both an insightful overview of the emerging field of wireless sensor networks, and an in depth treatment of algorithmic signal and
information processing issues. An excellent text for both professionals and students!? ?Deborah Estrin, Center for Embedded Networked Sensing,
UCLA
Designing, implementing, and operating a wireless sensor network involves a wide range of disciplines and many application-specific constraints. To
make sense of and take advantage of these systems, a holistic approach is needed?and this is precisely what Wireless Sensor Networks delivers.
Inside, two eminent researchers review the diverse technologies and techniques that interact in today?s wireless sensor networks. At every step, they
are guided by the high-level information-processing tasks that determine how these networks are architected and administered. Zhao and Guibas begin
with the canonical problem of localizing and tracking moving objects, then systematically examine the many fundamental sensor network issues that
spring from it, including network discovery, service establishment, data routing and aggregation, query processing, programming models, and system
organization. The understanding gained as a result?how different layers support the needs of different applications, and how a wireless sensor network
should be built to optimize performance and economy?is sure to endure as individual component technologies come and go.
Features
Written for practitioners, researchers, and students and relevant to all application areas, including environmental monitoring, industrial sensing and
diagnostics, automotive and transportation, security and surveillance, military and battlefield uses, and large-scale infrastructural maintenance. Skillfully
integrates the many disciplines at work in wireless sensor network design: signal processing and estimation, communication theory and protocols,
distributed algorithms and databases, probabilistic reasoning, energy-aware computing, design methodologies, evaluation metrics, and more.
Demonstrates how querying, data routing, and network self-organization can support high-level information-processing tasks.
© 2004 by Elsevier Inc. All rights reserved.
69.3 Yang, G.Z. Body Sensor Networks, Springer-Verlag London 2006
From the Publisher:
The last decade has seen a rapid surge of interest in new sensing and monitoring devices for healthcare and the use of wearable/wireless devices for
clinical applications. One key development in this area is implantable in vivo monitoring and intervention devices. Several promising prototypes are
emerging for managing patients with debilitating neurological disorders and for monitoring of patients with chronic cardiac diseases. Despite the
technological developments of sensing and monitoring devices, issues related to system integration, sensor miniaturization, low-power sensor interface
circuitry design, wireless telemetric links and signal processing have still to be investigated. Moreover, issues related to Quality of Service, security,
multi-sensory data fusion, and decision support are active research topics. This book addresses the issues of this rapidly changing field of wireless
wearable and implantable sensors and discusses the latest technological developments and clinical applications of body-sensor networks.
© Springer-Verlag London Limited 2006
136
69.4 Terrance J. Dishongh, Michael McGrath and Ben Kuris, Wireless Sensor Networks for
Healthcare Applications, Artech House, Boston 2008
From the Publisher:
Unlike other books on wireless sensors networks, this unique reference focuses on methods of application, validation and testing based on real
deployments of sensor networks in the clinical and home environments. Key topics include healthcare and wireless sensors, sensor network
applications, designs of experiments using sensors, data collection and decision making, clinical deployment of wireless sensor networks, contextual
awareness medication prompting field trials in homes, social health monitoring, and the future of wireless sensor networks in healthcare.
© Artech House, Boston 2008
Back to Sensors
137
70 Broadband Proliferation
138
71 Broadband Proliferation
Broadband access technology of whatever type (DSL, WiMAX, Satellite etc) is a key enabler technology for home healthcare monitoring, particularly if
personal health records are to be employed. Many of the product offerings currently available for home use will work with the old fashioned modem
dial-up (56kB) technology in a store-and-forward capacity, however this technology is not ideal for regular data forwarding or exchange of large files
(such as may be required with an personal health record). Also to ensure privacy and security, the data packets may contain a lot of overhead and thus
small packages may become very large (and slow and unreliable to transmit). Therefore access to a broadband technology is important for
telehealthcare. When broadband is discussed, three areas are of primary concern, these are;
• Coverage - According to the OECD "Since December 2004, broadband subscribers in the OECD have increased by 187%, reaching 221
million in June 2007 and 380 million in September 2008. Broadband is available to the majority of inhabitants even within the largest OECD
countries. A number of countries have reached 100% coverage with at least one wired broadband technology and up to 60% with coverage by
two".
• Speed - At the end of 2004 the average DSL speed across the OECD was less than 2 Mbit/s. The average speed of advertised connections
increased from 2 Mbit/s in 2004 to almost 9 Mbit/s in 2007. However the actual speed delivered to the customer can vary greatly form the
'advertised' speed and this has been an issue of some contention and has led to a lack of trust by consumers.
• Cost ? Cost of a broadband service will be a key factor in the uptake of home health monitoring technologies. Broadband is viewed as an
enabler technology for home healthcare and bearing in mind that the major cost items will be the hardware devices, software, monitoring etc,
it is critical that broadband pricing remains very low. According to a recent OECD report between 2005 and 2006 the average price for a DSL
connection fell by 19% and by 16% for cable Internet connections. Broadband is also affordable in most OECD countries. The price of a
broadband subscription in 20 of the 30 OECD countries was less than 2% of monthly GDP per capita in October 2007.
A detailed treatment of Broadband types, proliferation, cost, speed and policy is given in the Connectivity CAPSIL
139
72 BSN Node
72.1 BSN Node v3
BSN node is a hardware platform designed for the ease of the development of pervasive health care system. With a 8MIPS processor, a Zigbee ready
RF link, a 512K EEPRM and a stackable design, the BSN node provides a low-powered, miniaturised, and intelligent platform for the development of
pervasive physiological and context aware sensors.
http://vip.doc.ic.ac.uk/bsn/images/bsn-2009/BSN_node_v3.jpg
Sensing: Sensing abilities possible via the boards channels
I/O:
• 2 USART
• 2 channels DAC
• 8 channels 12-bits ADC
Chipcon CC2420
Radios:
• IEEE 802.15.4 (2.4GHz DSSS)
• Miniaturised chip antenna
• Range 50m (indoors) 125m (outdoors)
TI MSP430 ultra low power processor
CPU:
• 16 bits RISC processor
• 64KB +256B Flash memory
Storage: 4MB external EEPROM
72.3 Applications
The BSN hardware platform was designed to be used for the development of pervasive health care systems. See BSN for a list of applications.
72.4 Power
• The BSN node consumes 330uA at active mode, 1.1uA in Standby mode and 0.2 uA at sleep mode.
• Power to the board is provided by a specifically designed battery board.
72.5 Software
TinyOS
• BSN Node Hompage
• Wapedia - Body Sensor Network
• Wikipedia - BSN
• BSN Workshop 2009
72.7 Papers
• B. Lo, S. Thiemjarus, R. King, and G.Z. Yang. Body sensor network a wireless sensor platform for pervasive healthcare monitoring. In
Adjunct Proceedings of the 3rd International Conference on Pervasive Computing, May 2005.
Back to Sensors
140
73 BTnode rev3
73.1 BTnode Rev3
BTnode Rev3 is a compact dual radio device used for fast proto-typing of sensor and ad-hoc networks. It makes use of the Atmel AVR microcontroller
and is very simliar to the Crossbow Mica2 mote, differing in the amount of SRAM (256K) and the addition of the BluetoothTM radio.
http://www.btnode.ethz.ch/pub/uploads/Documentation/btnode_rev3.22-antenna5.jpg
BTnode Rev3 with wire antenna
Sensing: Generic sensor board extensions
External Interfaces
I/O:
• UART, SPI, I2C? , GPIO, ADC, Clock, Timer
• 4 Colored Status LEDs
• Reset button
Connectors
• Extension Connector J! Hirose Receptacle DF17-40DS
• Debug Connector J2 Molex 53261-1590
802.15.4 Radio
• Chipcon CC1000 radio operating at 868 MHz
• Option between integrated monopole antenna (default) or an external wire and external
co-axial connector (MMCX type).
Radios:
Class 2 BluetoothTM Radio
• Zeevo ZV4002 Bluetooth radio running HCI firmware.
• Supports the following features: Features: 0xff 0xff 0x05 0xf8 0x1b 0x18 0x00 0x80 <3-slot
packets> <5-slot packets>
CPU:
• Atmel ATmega 128L (8 MHz @ 8 MIPS), 4kByte EEPROM, 64kByte SRAM, 128kByte
Flash
• System Clock, 32 kHz real time clock and 7.3728 MHz system clock
• In System Programming* through serial ISP programmer, JTAG or resident bootloader
Storage: 3x60 kByte low power SRAM.
73.3 Applications
BTnode Rev3 is mainly used as a research platform in mobile and ad-hoc connected networks (MANETs) and distributed sensor networks.. It is
currently being used in two key projects:
• NCCR MICS
• Smart-Its
73.4 Power
The standard power supply for the BTnode Rev3 are two primary/rechargeable AA batteries in the range of 2-3V DC. The device also contains a boost
converter with an input range of 0.5-3.3V DC and an alternate supply input via the VDC_IN pin in the range of 3.6-5.0V DC.
• Primary Supply Linear LTC3429, 600mA max., 0.5-4.4V to 3.3V.
• Alternate Supply LT1962, 300mA max. 3.6-5.0V to 3.3V.
• Secondary Supply IO Default OFF.
• Secondary Supply BT Max. dissipation 80 mA. Default ON.
• Secondary Supply CC Max. dissipation 30 mA. Default OFF.
• Battery Charge Indicator
• On/Off Switch for the primary power supply.
• Current Access Shunts to all subsystems for in-situ power profiling (AVR, BT, CC).
141
73.4.1 Power Consumption
BTnode rev3 Bluetooth1
Battery Supply
BTnode rev3 Low Power Radio1
2 AA cells primary/rechargeable
Regulated Supply
Yes
Battery Capacity
2900 mAh
Minimum Vin
0.85 V
CPU sleep, Radio off
9.9 mW
CPU on, Radio off
39.6 mW
CPU on, Radio listen
92.4 mW
82.5 mW
CPU on, Radio RX/TX
105.6 mW
102.3 mW
CPU on, Bluetooth Inq
198 mW
---
CPU on, Radios both listening
Max. Power
135.3 mW
198 mW
102.3 mW
Table source: [1]
1all
values where measured on a live system running at 3.3V
73.5 Software
• BTnut System Software 1.8 available at both the BTnode Homepage and Sourceforge
For additional information on the BTnode, please see the following sites:
• BTnodes - A Distributed Environment for Prototyping Ad Hoc Networks
• BTnodes project page at Sourceforge
73.7 Papers
• BEUTEL Jan; KASTEN Oliver; MATTERN Friedemann; RÖMER Kay; SIEGEMUND Frank; THIELE Lothar; "Prototyping wireless sensor
network applications with BTnodes", Lecture notes in computer science, Wireless sensor networks, Jan 2004, vol. 2920, pp. 323-338
142
74 Bulgaria
74.1 Reimbursement Model in Bulgaria
A comprehensive policy on telehealth does not exist yet. A draft for National Health Strategy for 2009 referring to telemedicine and care for the elderly
has been submitted for discussion in the Parliament. So far, the Ministry of Health and the National Health Insurance Fund (NHIF) have put their efforts
into implementing a Strategy for the Introduction of E-Health in Bulgaria, in place since 2006. In reference to this, Inter Component Ware (ICW), in
cooperation with Cisco Bulgaria and a Bulgarian IT company ? Kontrax, ran a small-scale pilot project in 2007, connecting four pharmacies, seven GPs
and 1000 participants in two villages near Sofia for the introduction of the first electronic health cards.
Back to Business Models
143
75 California
75.1 California Reimbursement Model
• The Medicaid agency recognizes physician consultations (medical & mental health) when performed using interactive video teleconferencing.
• Payment is on a fee-for-service basis, which is the same as the reimbursement for covered services furnished in the conventional,
face-to-face manner. Reimbursement is made at both ends (hub and spoke sites) for telemedicine services.
• The state uses consultative CPT codes with the modifier "TM" to identify telemedicine services.
144
76 CardioNET MCOT three lead ECG monitoring system
http://www.cardionet.com/medical_02.htm
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77 Cardionetics C.Net 5000
http://www.cardionetics.com/cnet5000.php
146
78 Cardionetics C.Net5000 - 24-Hour Ambulatory ECG Monitor with Instant
Analysis
http://www.cardionetics.com/cnet5000.php
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79 Chipcon CC1000
79.1 Chipcon CC1000
The CC1000 is a single-chip true UHF transceiver which works in the 300Mhz to 1GHz range of frequencies. Its intended use was originally for the ISM
and SRD bands but its versatility allows it to be programmed to any other frequency in the range if desired. It is designed for low power applications and
is based on Chipcon's SmartRF® technologhy in 0.35?m CMOS.
79.2 Applications
Typical applications for the CC1000 as proposed by the datasheet are:
• Very low power UHF wireless data
• Home automation transmitters and receivers
• Wireless alarm and security systems 315 / 433 / 868 and 915 MHz ISM/SRD band systems
• AMR ? Automatic Meter Reading
• Low power telemetry
• RKE ? Two-way Remote Keyless Entry
• Game Controllers and advanced toys
79.3 Features
• RSSI output
• True single chip UHF RF transceiver
• Single port antenna connection
• FSK data rate up to 76.8 kBaud
• Frequency range 300 ? 1000 MHz
• Complies with EN 300 220 and FCC CFR47 part 15
• Integrated bit synchroniser
• High sensitivity (typical -110 dBm at 2.4 kBaud)
• Programmable frequency in 250 Hz steps makes crystal
temperature drift compensation possible without TCXO
• Programmable output power ?20 to 10 dBm
• Suitable for frequency hopping protocols
• Small size (TSSOP-28 or UltraCSPTM package)
• Development kit available
• Low supply voltage (2.1 V to 3.6 V)
• Very low current consumption
• Easy-to-use software for generating the CC1000 configuration
data
• Very few external components required
• No external RF switch / IF filter required
79.4 Interfacing
The CC1000 comes in a choice of two packages:
• TSSOP-28
• UltraCSPTM
(see datasheet for specific dimensions)
79.5 Configuration
The CC1000 is programmed via 3-wire serial bus thus making CC1000 a flexible and easily programmed transceiver.
79.6 Transmission
Three modes of transmission are offered:
• UART
• NRZ
• Manchester (aka Phase Encoding)
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79.7 Currently Used In
• BTnode rev3
• CRICKET
• MICA2
79.8 References and Additional Information
• CC1000 Datasheet
• Few facts about CC1000 chip
149
80 CODA
The codamotion system from Charnwood Dynamics Ltd is a solution for for real-time 3D motion measurement and analysis. It can be used for laboratory
based gait analysis, biomechanics research, sports science and ergonmics research.
150
81 CodeBlue
The CodeBlue system developed at HArvard is a prototype system developed to take body sensor netorks beyond lab/prototype stage and bring the
system in to real clinical settings. The CodeBlue system is a combined hardware and software platform and provides protocols for device discovery and
publish/subscribe multihop routing as well as a query interface tailored for medical monitoring. The CodeBlue system developed ECG, Pulse Oximeter
and motion activity sensors based on th MicaZ and Telos mote platforms. The testbed system featured a 30 node network and has demonstrated
scalability and robustness (with varying system parameters such as numbers of queries and data rates). CodeBlue also demonstrates robustness and
reliability with mobility. The CodeBlue team are collaborating with several hospitals and medical research groups that plan to make use of the CodeBlue
platform. These include Boston Medical Center, Brigham and Women?s Hospital, the Spaulding Rehabilitation Hospital, and Johns Hopkins University
CodeBlue ECG sensor
CodeBlue EMG and motion sensor
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CodeBlue Pulse Oximeter sensor
152
82 Communications
Communications ? Determine the most appropriate wireless communications solutions and protocols for BSN applications. The effects of body
interference will be reviewed and approaches which address this issue identified. Included will be a discussion of approaches to compensate for
environment interferences such source such as wireless AP points, microwave oven etc. Weakness in the current protocols being used for BSN such as
Bluetooth and 802.15.4 will be identified and recommendations for enhancements to the existing and emerging standards which will make them more
appropriate for task will be developed.
• Radio Transceiver
• Low Power Antenna Design
• Cross Tier Communications
• Standards
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83 Comparison of the internetwroking technologies
Back to Connectivity
154
84 Context Aware and Autonomic Sensing
From a data analysis point of view, context can be regarded as different levels of detail linked to physical and perceptual representations. A user's
cognitive activities are normally described at an abstract level, whereas the recognition of the physical status of a person is usually more descriptive and
data-driven. Context aware sensing generally aims at answering some of the following questions:
• Who: the identity of people in an environment
• What: the activities and interactions in the current environment
• Where: environment and location details
• When: timestamps and temporal relationships between events
• Why: States of a person, relationships between events and environmental factors.
From a sensing perspective, most of the context aware applications are based on motion sensors, physiological sensors (such as heart rate and
respiratory sensors), visual sensors (cameras, using off-line and real-time processing) and ambient sensors that provide environmental information.
The following table summarises some recent applications, more ideas on context aware sensing can be found in Thiemjarus et al.:
Link
Sensors used
Context awareness techniques
Purpose of study
Bao and Intille,
2006
Bi-axial accelerometer on hip, waist and ankle Decision tree classifiers/Naive Bayes
Activity Recognition from user annotated data
Ermes et al.
2008
Accelerometers on wrist and waist, ECG
sensor, respiration sensor, magnetometers
Decision tree and an MLP (Multilayer
perceptron)
Detection of daily activities and sports indoors
and outdoors
Krause et al.
2006
SenSay Prototype including a SenseWear
arm-band, a headset and a back-pack
KSOM (Kohonen's Self Organising
Map)
Identify context states without supervision
based on wearable sensors for a context
aware mobile phone.
Lo et al. 2007
An ear worn activity recognition sensor
Naive Bayes classifier based on
Gaussian class models
Activity recognition in a pervasive home
environment
Loosli et al. 2005
EMG, Blood volume pressure, skin
conductivity and respiration sensors
One class SVM and rupture detection Context change detection
Maurer et al.
2006
multisensor platform (eWatch) worn on
different positions
LDA then decision tree classifiers
Analysing the tradeoff between recognition
accuracy and computing complexity
Combining GPS measurements with
Dynamic bayesian Network for an
Estimating both activity and spatial context
accelerometers, microphones, light and
activity model showing locations and
over time
temperature sensors
environment
From a methodology point of view, the following list summarises some of the widely used techniques for context aware sensing:
Subramanya et
al. 2006
• Hidden Markov Models (HMMs) and their variants, including coupled HMMs,abstract HMMs and variable-length HMMs. A detailed webpage
linking to several recent papers as well as code and methods is available.
• Conditional Random Fields: CFRs model the conditional probability of the label sequence rather than the joint probability of both labels and
observations (as in HMMs). As above, a detailed [webpage] with resources on CRF is available.
• Bayesian network approaches, which are graphical models that encodes probabilistic relationships among variables of interest. A website
summarising methods and applications of Bayesian networks is available.
• Discriminative classifiers: aiming to discriminate between activities without actually modelling activity classes or behaviour variability. This
includes several classifier types, such as Gaussian Mixture Models (GMMs), Multi-layer perceptrons (MLP), Radial-basis functions (RBFs),
K-nearest neighbours (KNN) and K-means classifiers. A summary webpage with links to several toolboxes and applications is available.
84.1 Autonomic Sensing
The eight defining characteristics of an autonomic system are defined (the IBM website) as:
1. Self-management: An autonomic computing system needs to "know itself" - its components must also possess a system identity. Since a
"system" can exist at many levels, an autonomic system will need detailed knowledge of its components, current status, ultimate capacity, and
all connections to other systems to govern itself. It will need to know the extent of its "owned" resources, those it can borrow or lend, and
those that can be shared or should be isolated.
2. Self-configuration: An autonomic computing system must configure and reconfigure itself under varying (and in the future, even
unpredictable) conditions. System configuration or "setup" must occur automatically, as well as dynamic adjustments to that configuration to
best handle changing environments.
3. Self-optimisation: An autonomic computing system never settles for the status quo - it always looks for ways to optimize its workings. It will
monitor its constituent parts and fine-tune workflow to achieve predetermined system goals.
4. Self-healing: An autonomic computing system must perform something akin to healing - it must be able to recover from routine and
extraordinary events that might cause some of its parts to malfunction. It must be able to discover problems or potential problems, then find an
alternate way of using resources or reconfiguring the system to keep functioning smoothly.
5. Self-protection: A virtual world is no less dangerous than the physical one, so an autonomic computing system must be an expert in
self-protection. It must detect, identify and protect itself against various types of attacks to maintain overall system security and integrity.
6. Self-adaptation: An autonomic computing system must know its environment and the context surrounding its activity, and act accordingly. It
will find and generate rules for how best to interact with neighboring systems. It will tap available resources, even negotiate the use by other
systems of its underutilized elements, changing both itself and its environment in the process -- in a word, adapting.
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7. Self-integration: An autonomic computing system cannot exist in a hermetic environment. While independent in its ability to manage itself, it
must function in a heterogeneous world and implement open standards -- in other words, an autonomic computing system cannot, by
definition, be a proprietary solution.
8. Self-scaling: An autonomic computing system will anticipate the optimized resources needed while keeping its complexity hidden. It must
marshal I/T resources to shrink the gap between the business or personal goals of the user, and the I/T implementation necessary to achieve
those goals -- without involving the user in that implementation.
These areas are essential research issues for Body Sensor Networks and merit a detailed discussion. However, from a data-processing point of view,
collaborative information processing (leading to self-optimisation) is of relevance to data-processing in autonomic sensing and will be explained further
in the following section.
84.2 Collaborative Information Processing
In addition to considerations of single-platform signal processing, the networked information processing is further constrained by application
requirements on energy efficiency, network latency, and fault tolerance. Wireless Sensor Networks (WSNs) are severely constrained in computation and
communication capabilities due to the cost and size of available sensors. On the other hand, autonomic computing (AC) offers a promising solution to
manage large-scale computing systems without human intervention. Thus, the last decade has witnessed a rising interest in the use of Autonomic
computing techniques for sensor networks. A preliminary power aware self-configuring and self optimising sensor is given in Kang et al. The simulation
results confirm that the proposed power-aware scheme prolongs the network lifetime and balances the energy insensor nodes. A special issue of IEEE
Signal Processing Magazine focussed on collaborative signal and information processing in micro-sensor networks was published in 2002.
Zhao et al. use collaborative information processing to provide a solution for a sensor network tracking an object. In particular, tracking was used as a
canonical problem to expose important constraints in designing, scaling, and deploying sensor networks. Results from simulations and experimental
implementations demonstrate information based approaches can be scalable and make efficient use of scarce sensing and communication resources.
Beam-forming is one of the simple, yet important applications of collaborative information processing. In beam-forming, the signals obtained at different
receivers are used to cancel out noise and re-obtain the original signal sent by the source. In addition to the re-construction of the source signal and the
localisation of the source, beam-forming can help in source separation as different sources can be separated based on their locations.
IPSN (Information processing in sensor networks conference), since 2001, has presented a forum where several relevant papers were discussed, such
as D'Costa et al., Liu et al. and Whitehouse et al.
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85 CONTEXT Project - Contactless Sensors for Body Monitoring Incorporated in
Textiles
Musculoskeletal disorders, such as low back pain and RSI, often caused from psychological stress and physiological strain, are today among the
biggest health and safety problems for people world wide, leading to enormous social and economical costs. The drive for miniaturization of electronic
components should enable us to surround ourselves with sensors for monitoring our surroundings and our own body. However, body-monitoring
sensors today cannot be used in unobtrusive due to their shape or material properties.
The objective of the CONTEXT project was to create a system where different types of contactless sensors are incorporated into textiles to be used in
continuous monitoring of individuals. Contactless sensors were developed for the purpose of measuring electromyography (EMG) and
electrocardiography (ECG) signals.
The contactless sensors investigated in the CONTEXT project consist of true textile sensor electrodes that pick up the muscle and heart electric signals
and miniaturized pre-processing sensor electronics connected to a textile substrate which contains conductive yarn structures for data and power
transmission. Dedicated system architecture and data processing equipment were designed. Finally a feasibility prototype was created in the form of a
vest, containing two-dimensional sensor arrays integrated in a conductive textile substrate, capable of monitoring the muscle activity and the
psychological stress state of the person wearing it.
Woven Textile with Contact Points
EMG sensor developed
The new textile structures are characterized by high mechanical flexibility, high bend and break resistance and therefore a very good reliability and
durability. These structures offer the possibility for a complete textile integration with a substantial improvement of the wearing comfort.
Hockey Hit application being developed
One of the ideas that has been developed within the project is a concept for application in sports: a wearable device that measures aspects of the
hockey hit and translates it into feedback that is relevant for the user. ?Your Move? is a wearable measure-ment and feedback system developed for
field-hockey players (talented youth aged 10 to 14) to improve the regular hit. Two technologies, contactless EMG measurement and motion capturing
(using Xsens technol-ogy), are used to measure forward motion, body posture, and speed and accuracy of the ball. A first visual and functional
demonstrator has been developed, consisting of a vest and a base plate with integrated sensors. In first tests with hockey players at the Oranje Zwart
Field Hockey Club in The Netherlands, it was shown that ?Your Move can really help hockey players to improve their hit and can also be further
developed into products that can be used to improve different skills, also in other sports.
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ConText is currently drafting a business plan for three potential applications: anti-RSI garment, hockey hit analyser and an active EMG game controller.
Back to Useability
158
86 Cost and Reimbursement
A recent Accenture report (1) noted that ?Cost and complexity continue to hold back the development and adoption of converged digital home
solutions?. The survey findings indicate that more than three-quarters (80%) of consumers cite cost as the number one barrier to purchasing a digital
home solution. A 2004 Frost and Sullivan Report (2) stated ?Foremost among these [obstacles to telemonitoring growth] is the challenge of gaining
reimbursement in a market perceived to be lacking the critical mass and evidence of cost-effectiveness. The absence of reimbursement impacts
manufacturers investing in the innovation, adoption, and deployment of telehealth programs. While technology is a strong differentiator, industry
participants realize that the prime driver of the market is reimbursement".
Therefore the evidence suggests that people in general are unwilling to invest in out-of-pocket home healthcare monitoring technologies at this time. A
major reason for this comes down to the lack of awareness of the benefits of the technology placed alongside the out-of-pocket costs of purchasing the
technology. As there are no coherent and consistent reimbursements strategies available from healthcare insurers (other pay-for-service or case by
case type models), at present this is a cost that people are not willing to forego. Major progress on reimbursement, incentivisation and risk management
is therefore needed and will probably be the single biggest factor in the adoption of home health monitoring solutions particularly among older people.
Aside from the patient, there is also very little incentive for the healthcare provider to get involved in telemonitoring. Take for example a physicians
practise with a busy staff and patient base. For the practise to get involved there needs to be some form of out-of-pocket payment from the physicians
(PCs, server, and software licenses etc). As there are no means of re-cooping this from insurers, the physician has to essentially take a risk on adoption
of the technology. Also, if the physician misinterprets data from a telemonitored patient, he/she will potentially be sued with no insurance protection to
fall back on. So why should he/she take this risk?. Best for him/her to wait until all these issues have been ironed out and the proper policies,
best-practises (and protection) is in place.
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87 References
• 1. http://digitalforum.accenture.com/DigitalForum/Global/ViewByTopic/NewMediaTechnologies/0705_digital_home_study.htm
• 2. Frost and Sullivan, Overview of the U.S. Diabetes Remote Patient Monitoring Devices Market August 29, 2004]
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88 CRICKET
88.1 Cricket V2
The MCS410CA, Cricket Mote, is a location aware mote that contains all the same hardware specifications as the MICA2 mote. The device uses a
combination of RF and Ultrasound technologies to establish a differential time of arrival (TDOA) thereby producing linear range estimations. The Cricket
Mote is a joint venture between Crossbow and MIT.
http://www.willow.co.uk/assets/images/cricket_ani.gif
Sensing: Ultrasound Transmitter and Receiver with a range of 10.5m
• Direct connect RS232 serial port
• 51 pin connector for connection to a sensor board
• 3 Diagnostic LEDs
• External power connector
I/O:
433 Mhz Transceiver (CC1000)
Radios:
• Range up to 30m indoors
• Port available to connect an external radio antenna
Atmega 128L Microcontroller
CPU:
• 128K Flash
• 4K SRAM
• 4K EEPROM
• up to 16 MIPS
Storage:
88.3 Applications
The Cricket is used for indoor localization, providing information such as position coordinates, space identifiers and orientation to applications running on
portable media such as laptops, handhelds (PCs or Mobile phones) and sensor nodes.
88.4 Power
The Cricket is equipped with a battery pack but may be powered via the external power connector also.
• Battery pack requires two standard AA batteries.
• External power supply must provide 3-6 volts regulated at 300-1000mA
88.5 Software
The new version of Cricket (v2) is programmed via TinyOS. Additional software to be used with the Mote may be found here.
• Cricket - Crossbow
• Cricket Mote Datasheet
• Cricket Mote Platform - WILLOW Technologies
• The Cricket Indoor Location System
• Cricket v2 User Manual
88.7 Papers
• Nissanka Bodhi Priyantha, Hari Balakrishnan, Erik Demaine, Seth Teller, Mobile-Assisted Localization in Wireless Sensor Networks,
Proc. IEEE INFOCOM Conference, March 2005.
• Adam Smith, Hari Balakrishnan, Michel Goraczko, Nissanka Priyantha,Tracking Moving Devices with the Cricket Location System, Proc.
2nd USENIX/ACM MOBISYS Conf., Boston, MA, June 2004.
• Nissanka Bodhi Priyantha, The Cricket Indoor Location System, PhD Thesis, Massachusetts Institute of Technology, June 2005.
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• Kevin John Wang, An Ultrasonic Compass for Context-Aware Mobile Applications M. Eng. Thesis, Massachusetts Institute of
Technology, June 2004.
Back to Sensors
162
89 Data Mining and Trend Analysis
With the large amounts of data obtained from sensors, and the availability of storage space, the role of data-mining in wireless sensor networks is to
provide techniques for the analysis of data, identifying underlying rules and features, discovering patterns and trends. Usually, data-mining tends to work
from the data up, and the most efficient techniques are normally those developed with an orientation towards large volumes of data. IBM has identified
two types of models that can be used to mine information: 1- The verification model, which takes a hypothesis from the user and tests the validity of it
against the data and 2- the discovery model, which requires the system to automatically discover frequently occurring patterns and trends without
intervention or guidance from the user. Data-mining is a very important topic in BSN data analysis and it encompasses many areas that can be
expanded. However, in this section, we will focus on several issues that are of importance to data processing in BSN. These are:
• Data-Preprocessing
• Data visualisation
• Descriptive Modelling and Clustering
• Predictive Modelling
• Pattern Mining
89.1 Data-Preprocessing
Since sensor data can be noisy, inconsistent or incomplete, preprocessing data is needed as a first step before data-analysis. The tasks in
data-preprocessing include:
• Data integration: involves combining data at different sources and providing the user with a unified view.
• Data cleaning: filling in missing values, smoothing noisy data and removing outliers.
• Data transformation: aggregation of data and normalisation.
• Data reduction and discretisation: reducing the number of attributes by removing irrelevant attributes or dimensionality reduction (see Sensor
Fusion page). Other methods include aggregation, sampling and clustering.
89.2 Data Visualisation
In many cases, visualising data before analysis offers the user some guidance on existing patterns in a dataset as well as relationships between
variables. However, high data dimensionality and large amounts of data could prevent the user from visualising all the data available in a database.
Thus, methods of reducing the data (mentioned above) could be used before visualisation. Summary variables can be created to summarize important
data, examples are: Range, variance, mean, median, maximum, minimum, covariance and correlation matrices. Single variable plots showing
histograms and scatter plots including several variables can be used to observe data relationships and outliers. Projection methods (detailed in Sensor
Fusion) can be used to project multivariate data into 2 dimensions. Projection pursuit is a method that involves finding the most 'interesting' possible
projections in multivariate data. Projections that deviate from a Normal distribution are considered to be more interesting. As each projection is found,
the data is reduced by removing the component along that projection. The process is repeated to find new projections. Interesting links are:
-IEEE Visualisation week
-NASA's scientific visualisation studio
-National Visualisation and Analytics centre
89.3 Descriptive Modelling and Clustering
Descriptive models generally provide a summary of the data which could lead to the observation of the main trends that exist in a dataset. In this section
we will cover some of the widely used techniques in this area:
89.4 Probabilistic distributions
In many applications, data can be described by using probabilistic distributions. Some of the methods used are:
• Parametric models: Include the use of a single Gaussian or a mixture of Gaussians, among other methods of modelling probabilistic
distributions (Bernoulli, Poisson). Gaussian Mixture models(GMM) aim at modelling several components (Gaussians) weighted by a
parameter. Advantages of using GMMs are that they have well studied statistical inference techniques, offer a flexibility in using component
distributions, and allow obtaining a density estimation for each cluster.
• Non-parametric models: Include Kernel density estimation, but some can be computationally expensive to compute on large datasets.
• Graphical models: Probabilistic graphical models provide a compact representation of joint probability distributions. The nodes represent
random variables and arcs represent dependence between these variables. Graphical models can be directed or undirected. The latter is
used more to express causality between variables. Some tutorials on graphical models are available on: Murphy: A Brief Introduction to
Graphical Models and Bayesian Networks and Jordan: An introduction to variational models for graphical models. Applications to activity
163
recognition are summarised in the following web page.
89.5 Clustering
Clustering is the grouping of data into subsets or clusters, where the items (data) in each cluster share common traits. Clustering is a widely used
method in data mining, some of the clustering methods include:
• k-means: ASsigns each point to the cluster whose centroid is nearest. An interactive demo is available on the following webpage. Code and
examples are available in the Netlab toolbox.
• Fuzzy c-means clustering: A method that allows a datapoint to belong to more than one cluster. A tutorial is available on: Fuzzy c-means
clustering.
• QT (Quality threshold) clustering: QT clustering looks for clusters of data points such that each point in the cluster is within a specified
distance (based on a user-defined distance metric) of every other point in the cluster. This techniqueis widely used for gene clustering. More
info is available on: the GeneSpring Tutorial.
• Self Organising maps: The self-organising map consists of a regular unit of neurons that attempts to represent all observations with optimal
accuracy using a restricted set of models. At the same time the models become ordered on the grid so that similar models are close to each
other and dissimilar models far from each other. A webpage on SOMs is acailable which provides demos, software and research papers.
• Multi-dimensional scaling:This visualisation technique starts with a matrix of item-item similarities then assigns a location of each item in a
low dimensional space which can be used for graphing or 3D visualisation. More information and the relationship to factor analysis is available
on the following webpage.
• Hierarchical clustering: In hierarchical clustering, a series of partitions takes place, which may run from a single cluster containing all objects
to n clusters each containing a single object. Hierarchical Clustering is subdivided into agglomerative methods, which proceed by series of
fusions of the n objects into groups, and divisive methods, which separate n objects successively into finer groupings. Some more information
on hierarchical clustering is available on the following webpages: How to explain hierarchical clustering, a tutorial on clustering algorithms. An
application to energy efficiency in wireless sensor networks is available on BBandyopadhyay and Coyle.
89.6 Predictive Modelling
Predictive modelling involves both regression and classification. Regression models continuous valued functions whereas classification aims at
predicting class labels (both discrete and nominal).
Regression methods include: Neural networks, Kernel machines and Decision trees. Classification includes model based techniques that are aimed at
using a model based on training data for testing new data points, and discriminative techniques that aim at separating between classes without
specifically having to model each class. Model-based classification techniques include gaussian mixture models (GMMs)- mentioned above, Naive
Bayes and other Bayesian techniques. Temporal models aim at learning models of the data by taking into consideration its temporal patterns. These
include Hidden Markov Models, Conditional Random fields and Dynamic Bayesian Networks. A summary of discriminative models for classification is
available on the following web page.
89.7 Pattern Mining
Pattern mining is the task of finding existing patterns in data. In this context patterns often means association rules between variables. A survey on
pattern mining with a focus on association rules was compiled by Geothals. A summary of the current status and future directions is given in Jiawei et al.
While there is significant variability in human activity that can be observed from wearable sensor data, there is typically a repeating structure over the
long term. To this end, frequent pattern mining constitutes a promising technique for the discovery of rich routine information and has been successfully
applied to a number of applications. FP-Stream is an example of a frequent pattern mining algorithm that incorporates temporal information by mining a
data stream at different granularities. It constructs a prefix-tree of patterns, where each node specifies support for a pattern over time windows. Other
fast algorithms have been investigated for finding frequent patterns. Closet+, for example, is an algorithm which can quickly find closed frequent patterns
using optimized search strategies, and provides advantages over existing mining algorithms in terms of runtime and memory usage.Based on
FP-stream and Closet+, a data structure called the routine tree designed to describe behaviour patterns is proposed in Ali et al. . The routine tree is a
variable resolution mapping of time periods within a day to frequent patterns of activity levels.
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90 Denial of Service Attacks
Defending against denial-of-service attack DoS, which aim to destroy network functionality rather than subverting it or using the sensed information, is
extremely difficult. DoS attacks can occur at the physical layer?for example, via radio jamming. They can also involve malicious transmissions into the
network to interfere with sensor network protocols or physically destroy central network nodes. Attackers can induce battery exhaustion in sensor nodes
for example, by sending a sustained series of useless communications to the targeted nodes or by the creation of routing loops that will exhaust all
nodes in the network. Potential defenses against denial-ofservice attacks include techniques such as spread spectrum communication and frequencyhopping (to counteract jamming attacks). Proper authentication can prevent injected messages from being accepted by the network. However, the
protocols involved must be efficient so that they themselves do not become targets for an energy exhaustion attack. For example, using signatures
based on asymmetric cryptography can provide message authentication. However, the creation and verification of asymmetric signatures are highly
computationally intensive, and attackers that can induce a large number of these operations can mount an effective energy-exhaustion attack.
165
91 Denmark
91.1 Reimbursement Model in Denmark
In general, home telehealth services are not provided as mainstream so no standardised reimbursement procedures are in place for home telehealth
services as of yet. To the extent that some home telehealth services are available, eligibility (for free public services) would be determined by healthcare
clinics after an assessment of the patient. Services available on the private market must be paid for out-of-pocket. There are no national ventures or
overall national strategies running as of yet. A national program for telemedicine and home monitoring is under development and expected in 2009.
166
92 Design Aspects of Body Sensor Networks
92.1 Overview
The ultimate promise of body sensor networks is that they will become pervasive and operate unobtrusively behind the scenes without significant human
set-up or intervention. While a lot has been written about the technical aspects of body sensor networks, this section will be concerned with the design
aspects of body sensor networks. The focus here is less on hardware and technical specifications but more on how 'practical' and useable these devices
are and how easily they can integrate in to peoples lives unobtrusively. Examples will be presented both from academia and industry to demonstrate
these different design aspects.
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93 Key Design Considerations
There has been a realisation that if these sesnors are to get truly smaller according to Moores Law then there exists the possibility of these sensors to
become ubiquitous and operate reliably without any human set up. The early work regarding wireless sensor network has been very focussed on
technical details such as routing algorithms, quality of service and power optimisation and this work continues. A parallel strand of research now is
underway as to how in practical terms these sensors can interface with humans. For example sensor systems need to be mobile and reliable. As an
example, there is little point in system that fails if the person moves too much or if body temperature or sweat corrupts measurements. Also, the wearing
comfort of the sensor system is important, again there is little value in a system that needs to be so tight (to maintain good electrical contact) that it is
uncomfortable to wear or cuts off blood circulation. The ideal scenario of a body sensor network is one that is embedded in to an everyday item that we
use such as furniture, clothing, bed or jewellery. However these present design challenges which will be the topic of discussion in the following sections.
93.1 Useability
A requirement of a truly ubiquitous and pervasive monitoring system is that it is 'useable' i.e. it fits in to peoples lives unobtrusively and is 'practical'. This
section is focussed on initiatives that concentrate on these aspects of body sensor networks. Quite a lot of recent research has been focussed on
wearable sensors and sensors that can be integrated in to garments (clothes).
An early project in this area was the EU project WEALTHY Project - Wearable Healthcare Systems for vital signs monitoring (1) which aimed to weave
fabric material with piezoresistive materials to form a garment capable of measure ECG of patients undergoing cardiac rehabilitation. Further work was
undertaken by the EU funded MyHeart Project (2) which was a consortium of 33 partners in 11 countries led by Philips. The focus here again was to
achieve a 'practical' wearable sensor solution for measuring ECG reliably. A further EU project called Project STELLA - Stretchable Electronics for Large
Area Applications (3) was launched in 2006 and will run through 2010. It is entirely focussed on the concept of bendable and stretchable electronics i.e.
substrates and PCBs, and how these could be integrated in to wearable devices. The CONTEXT Project - Contactless Sensors for Body Monitoring
Incorporated in Textiles (4) project was a EU funded STREP project which completed in mid 2008 and developed more garment-type prototypes for
measuring physiological vital signs. A particular application investigated here is the area of body sensor networks in sports applications. The OFSETH Optical Fibre Sesnors Embedded in to technical Textiles for Healthcare monitoring (5) project used a slightly different principle to the previous projects
which mainly looked at the merging of electrical devices and textiles. The OFSETH project was concerned with using pure optical methods (instead of
electrical) of interfacing with textiles.
93.1.1 Global Efforts Concerning Useability
For a round up of some of what has been happening follow this link to see some Global Efforts Concerning Useability
93.2 Reliability and Stability
A major challenge with wireless sensor networks in general to date is how to ensure that communications is reliable and robust. Reliability can be
defined as the ability of the network to ensure reliable data transmission in a state of continuous change of network structure.
The Reliablity Dilemma - In typical wireless ad hoc networks, reliability and scalability are always inversely coupled. In other words, it becomes more
difficult to build a reliable ad hoc network as the number of nodes increases. This is due to the network overhead that comes with the increased size of
the network. In ad hoc networks, the network is formed without any predetermined topology or shape. Therefore, any node wishing to communicate with
other nodes needs to generate more packets than just the data packets i.e control packets. These extra packets are generally called "control packets" or
"network overhead". Increased overhead is unavoidable in a larger scale wireless sensor network to keep the communication paths intact. In typical ad
hoc networks, the overhead increases exponentially as the network size grows - therefore reliability and scalibility are closely related - they act against
each other. Other issues such as responsiveness and mobility are also on an inverse relation with newtork efficiency. For a network to be more
repsonsive (e.g. a mobile network) more control packets needed ,therefore more overhead (also more battery power). Some techniques to address
these issues are available including DynamicRouting
For smaller networks such a personal health monitoring and networks where there is a relative amount of static nodes, these effects are minimised,
however for larger networks such as in a hospital environment, where many nodes may be present, and there may be a lot of mobility, the issues of
security and reliability are of tremendous importance in ensuring reliable data transfer.
93.3 Security of Sensor Networks
Of major concern is the security aspects of sensor networks. As networks grow, the vulnerability of network nodes to physical and software attack
increases. Attackers can also obtain their own`commodity sensor nodes and induce the network to accept them as legitimate nodes, or they can claim
multiple identities for an altered node. Once in control of a few nodes inside the network, the adversary can then mount a variety of attacks?for example,
falsification of sensor data, extraction of private sensed information from sensor network readings, and denial of service attack. Therefore routing
protocols must be resilient against compromised nodes that behave maliciously. Ensuring that sensed information stays within the sensor network and is
accessible only to trusted parties is an essential step toward achieving security. Data encryption and access control is one approach. Another is to
restrict the network?s ability to gather data at a detail level that could compromise privacy. For example, in a healthcare environment storing data in an
anonymous format and removing any personal referencing information. Another approach is to process queries in the sensor network in a distributed
manner so that no single node can observe the query results in their entirety. This approach guards against potential system abuse by compromised
malicious nodes.
Some of the more common security considerations of any sensor network inculde the following; Eavesdropping and Denial of Service Attacks. See the
Privacy & Security Capsil for more information.
168
93.3.1 Examples of Systems Designed With Security and Reliability in Mind
Follow this link for details of some detailed Examples of Systems Designed with Security and Reliability In Mind
93.4 Biocompatability
The interface of the sensor node to the human is all important in body sensing networks. The material from which the sensor is made must be capable
of performing the sensing robustly while at the same time causing no side effect problems to the human interface (i.e skin or other surface). Examples of
such problems could be itching or rash or possibly even cell tissue damage. There is a lot of material that discusses the materials and treats the various
chemical/physical processes, however there is not a lot of material that deals explicitly with biocompatability and presents demonstrable solutions with
scaleable robust working prototypes.
In the book "Body Sensor Networks" (6) there is an excellent treatment of the issues of biocompatability of sensors. The key property of biocompatability
is for the surface area to be as large as possible. Fundamental issues such as sensor fouling, sensor adsorption, and the use of micro sensor arrays to
overcome these effects are discussed. The development of novel electrode made from Boron doped diamond films is also discussed as one promising
advance in the area of sensor biocompatability. Diamond is also attractive from a number of fronts in these appliactions due to its stability, chemical
inertness and low surface oxygen content, but again it is too early to say if this is truly a breakthrough development.
93.4.1 Examples of Systems Demonstrating Biocompatability
This linked section will detail Examples of Systems Demonstrating Biocompatability. This biocompatability is either explicit or inferred (no available
literature).
93.5 RF Emissions and Interference Aspects
Further issues to be considered here are the body effects on RF signals i.e. how the body effects RF signals. Higgins (7) has shown that antenna design
is crucial in body sensor networks and the increase in dielectric constant from having proximity to a human body actually works in the designers favour
in that smaller antenna can be used.(physically small antennas though do have their drawbacks also!). Other authors such as Alomainy et al (8) have
shown that antenna design must be carefully investigated with the proposed sensor and issues such as sensor size, orientation impedance matching,
gain and efficiency need to be carefully considered. Less reported also are the effects of RF on tissue, the so called heating effect of RF signals on
human cells. It is generally assumed that if power levels are kept low that and that if 'exposure time' i.e. time the device in 'on' is kept low (low duty
cycle), then this heating effect is negligible. However low power, low duty cycle can lead to unreliable networks so a compromise is needed. Also to be
considered are the potential sources of interference that can take place between similar wireless devices operating in proximity to each other and other
non-wireless devices operating in the area. For example an implanted pacemaker cannot be allowed to interfere with a wireless ECG monitor worn on
the body, or a 'noisy' flourescent light can not be allowed to interfere with a wireless pulse oxidation monitor.
Follow this link for more information on RF Emissions and Interference Aspects.
93.6 Privacy & Security
networks treat privacy through security mechanisms i.e. encryption and data protection throughout the hardware and software layers, however there is a
whole area of privacy that needs to be proactively addressed if sensor networks are to reach their true potential in a timely manner. Some of the key
Privacy concepts include:
• Fundamentals of Freedom
A popular dictionary defines privacy as: "The quality or condition of being secluded from the presence or view of others. The state of being free from
unsanctioned intrusion: a person's right to privacy". The commonly accepted definitions of Privacy that are built in to much legislation use the concepts
of a person?s right to be free from unreasonable search and seizure and intrusion. It also states that the protection of personal information is a
fundamental right. The United Nations Universal Declaration of Human Rights (9) which is exactly 50 years old, enunciates the fundamental right to
privacy and can be viewed at www.un.org/overview/rights.html . The EU Directive 95/46/EC "on the protection of personal data" (10) is the giuding policy
document within then EU. It affirms the right to privacy, transparency, legitimacy of purpose and proportionality. It also deals with trandfer of data
between boundaries inclusing third party countries (non-EU).
• OECD Guidelines on Privacy
The Organisation for Economic Cooperation and Development (OECD) released Guidelines for Data Protection and Privacy (11) in 1980 which was
based on the US initiated Fair Information Practises (FIPS) (12) policy. These Guidelines were reaffirmed in 1998 as still relevant and form the basis of
much legislation worldwide. These key principles form the basis of much privacy legislation world-wide and all wireless sensor network systems must at
a minimum comply with these guidelines.
The are major issues around privacy raised and ethics raised by the evolution towards a so called ubiquitous computing society and as the evolution
progresses, these issues become more important. In fact privacy/ethics will be a fundamental barrier to adoption unless handled proactively, as
169
presently technology has been outpacing policy.
For a more detailed treatment on these topics see the Privacy & Security Capsil.
93.7 Not Everyone Likes this Technology
With the above concerns in mind, many groups have sprang up that lobby and campaign against the ubiquitous deployment of wireless technology. As
RFID systems have been ahead of commercial wireless sensor networks they have been in a way the lightning rod for a lot of potential privacy issues
that wireless sensor networks will encounter and the issues are almost identical (actually they will be tougher for sensor networks as we move to truly
ubiquotous networks!). An example of a lobby group who are opposed to this kind of technology is called CASPIAN (Consumers Against Supermarket
Privacy Invasion And Numbering) (13). They refer to RFID devices as spychips and are concerned with personal information being used in an
unauthorised manner. Quoting from their website (spychips.com)... "We do believe, however, that these technologies pose serious risks to consumers,
and we have called on the world's shoppers to reject them. CASPIAN hopes to see both technologies (RFID and supermarket loyalty cards) ultimately
fail in the marketplace as a result of consumer opinion. In the long run, outright market failure would offer more effective consumer protections than
temporary legislative band-aids. (What the legislature grants, the legislature can easily take away, limiting the field of consumer espionage to itself."
This gives a flavour of some of the difficulties in the privacy debate and shows the need for it to be handled proactively and not 'bolted on' once the
technology is ready i.e. as an afterthought. The area of healthcare is one where the 'hearts and minds' debate can be easier to argue i.e. people would
be willing to trade off some privacy if their wellbeing or quality of life is improved. However for normally healthy people, the arguement can be lost if for
example as a result of remote monitoring, a persons home is broken in to and medication stolen or if information on a sensitive medical condition is
disseminated, or if a person finds that their medical insurance premiums are fluctuating based on data they collected at home (i.e. one bad day sees the
premium skyrocket....). The linkage of personal health data to 'other systems' such as insurance databases, national security etc (databases that are
ostensibly in the consumer interest) needs to be handled very carefully to avoid a backlash from consumers and ultimately market failure.
93.8 Examples of Body Sensor Networks Concerned with Design Aspects
93.8.1 Academic
Examples of Body Sensor Networks from Academia aimed at addressing some the Design considerations
• Eco: Ultra-Wearable and Expandable Wireless Sensor Platform
• WEALTHY Project - Wearble Healthcare Systems for Vital Signs Monitoring
• MyHeart Project
• Project STELLA - Stretchable Electronics for Large Area Applications
• CONTEXT Project - Contactless Sensors for Body Monitoring Incorporated in Textiles
• OFSETH - Optical Fibre Sesnors Embedded in to technical Textiles for Healthcare monitoring
• ALARM-NET- Assisted-Living and Residential Monitoring Network for pervasive, adaptive healthcare
• CodeBlue- Harvard University Mote based clinical sensor network system
• iShoe - Harvard/MIT iShoe
• Wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation - Jovanov et al (34)
93.8.2 Commercial
Here are some examples of body sensor networks that that have been commmercialised, thereby overcoming at least some of the design constraints
that we have been discussing. Obviously going from a lab prototype is a long journey in terms of certification and reliability and some of these systems
to date have proven very successful.
• Medtronic Reveal Insertable Loop Recorder (31)
• Cardionetics C.Net5000 - 24-Hour Ambulatory ECG Monitor with Instant Analysis (15)
• Kiwok AB - BodyKom SeriesTM ECG - Kiwok AB (32)
• CardioMEMS - EndoSure (17)
• Tunstall Fall Sensor (33)
93.9 Related EU Projects
• WEALTHY Project - Wearble Healthcare Systems for Vital Signs Monitoring
• MyHeart Project
• Project STELLA - Stretchable Electronics for Large Area Applications
• CONTEXT Project - Contactless Sensors for Body Monitoring Incorporated in Textiles
• OFSETH - Optical Fibre Sesnors Embedded in to technical Textiles for Healthcare monitoring
• Biotex Project - Biosensing Textiles for Health management
170
93.10 Summary and Recommendations For Further Work
There is quite an amount of information on aspects of sensor networks in healthcare applications and many of them are detailed here. There does
appear to be quite alot of institutions doing similar work in this area for example there are many published details on ECG, EMG, Pulse Ox etc
monitoring systems and many different approaches to the problem of reliable and robust monitoring. Interestingly there are very few published
end-to-end examples of systems that have been designed all the way to the patient (and back again). More work is certainly needed on aspects of end
to end reliability and security and demonstrating robustness in large sensor networks where parameters change frequently (data rates, number of
nodes, latency, mobility etc). There needs to be agreement on a 'standard' approach to such platforms where the underlying architecture, policies and
aspects of hardware are defined according to requirements such as HIPAA and the EU privacy directive.
Given the amount of information also on aspects of sensor newtrok design, there is very little information on biocompatability of sensor materials. Much
of the efforts here are at the basic research level (materials science, garment fabrication etc) and this is appropriate as the promise of wearable devices
is quite considerable. However there needs to be some initiatives aimed at investigating the relationship between the sensor interface and the human
body/skin, long term. Some of the commercial organisations with implantable devices have obviously performed a lot of work in this area, however
information is usually protected and so not much can be gleaned form lessons learned here.
More work also needs to be done of the RF effects of sensor networks on the human body. For example RF produces a heating effect which could
possibly damage human cells. Even with low emmitted and radiated power levels, it remains to be proven what the effect on human tissue over time
(and with many sensors on the body) would actually be.
If wireless sensor networks are to become truly pervasive and ubiquitous, the privacy debate will be a very tricky one indeed and one that needs to be
handled proactively. If it is not handled proactively then the technology can be developed and available but will sit on the shelf with no market demand!
Research needs to change course slightly from the technical areas and move into the societal, ethnographic and demographic areas. Concerns such as
profiling, 'big brother', 'one big database' etc need to be addressed up front and policies developed and agreed ahead of the technology becoming
mature.
93.11 References
• 1. http://www.wealthy-ist.com/
• 2. http://www.hitech-projects.com/euprojects/myheart/home.html
• 3. http://www.stella-project.eu
• 4. http://www.context-project.org
• 5. http://www.ofseth.org
• 6. "Body Sensor Networks" By Guang-Zhong Yang, Magdi Yacoub, Contributor Guang-Zhong Yang, Magdi Yacoub Published by Birkhäuser,
2006 ISBN 1846282721, 9781846282720
• 7. http://212.67.202.176/~armms/images/conference/1128506900.pdf
• 8. Akram Alomainy, Yang Hao and Frank Pasveer "Modelling and Characterisation of a Compact Sensor Antenna for Healthcare Applications"
http://www.elec.qmul.ac.uk/people/akram/papers/Philips_SensorAntenna_BSN2007.pdf
• 9. http://www.unhchr.ch/udhr/
• 10. http://ec.europa.eu/justice_home/fsj/privacy/law/index_en.htm
• 11. "OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data".
http://www.oecd.org/document/18/0,3343,en_2649_34255_1815186_1_1_1_1,00.html
• 12. "Fair Information Practice Principles". US Federal Trade COmmission. http://www.ftc.gov/reports/privacy3/fairinfo.shtm
• 13. http://www.spychips.com/
• Back to Sensors Page
171
94 Development Environments
A key element in the development of any BSN application is the software component. TinyOS is the dominant environment for the development of
embedded applications. However it can be a rather esoteric environment requiring experienced software developers which has in the past acted as a
barrier to wider adoption. Enhancements to the TinyOS environment will be discussed together with other application development environments which
simplify the firmware level software development task. Methods facilitating easy integrating of lower level software programs with application level
programs will be reviewed.
172
95 DSY25
95.1 DSYS25
The DSYS25 sensor platform developed by the Computer Science Department in University College Cork, Ireland is a 25mm x 25mm module with an
Atmel AVR ATMEGA 128 microcontroller and a Nordic nRF2401 radio.
http://www.cs.ucc.ie/misl/dsystems/images/sensor3.jpg
Sensing: User Defined
I/O:
• 2 serial ports
Radios:
CPU:
• nRF2401 (Nordic Semiconductor)
♦ GFSK Transceiver
♦ Frequency 2400-2524MHz
♦ Data rates from 0 to 1Mbps
♦ Address and CRC computation
♦ ShockBurstTM for ultra-low power and relaxed
MCU performance
• ATMEGA 128 4MHz
Storage:
95.3 Application
95.4 Power
95.5 Software
The DSYS25 uses TinyOS as its operating system as it is opensource and freely available. There is also a version of TinyOS specifically tailored to the
DSYS25 platform. See TinyOS for DSYS25 for details.
• http://www.cs.ucc.ie/misl/dsystems/HTML/dsys25.php
173
96 DSYS25
96.1 DSYS25
The DSYS25 sensor platform developed by the Computer Science Department in University College Cork, Ireland is a 25mm x 25mm module with an
Atmel AVR ATMEGA 128 microcontroller and a Nordic nRF2401 radio.
http://www.cs.ucc.ie/misl/dsystems/images/sensor3.jpg
Sensing:
User Defined
I/O:
Radios:
CPU:
• 2 serial ports
• 80 pin general purpose bus
• 40 pin bus for configuration and data transfer between layers
• 20 pin connector on the RF transceiver layer for four low noise analog input channels
• nRF2401 (Nordic Semiconductor)
♦ GFSK Transceiver
♦ Frequency 2400-2524MHz
♦ Data rates from 0 to 1Mbps
♦ Address and CRC computation
♦ ShockBurstTM for ultra-low power and relaxed MCU performance
• ATMEGA 128 4MHz
Storage:
96.3 Application
The DSYS25 has many applications ranging from traffic control to agriculture
96.4 Power
The DSYS25's power source is application specific, using either batteries or some other form of energy supply mechanisms such as solar cells.
96.5 Software
The DSYS25 uses TinyOS as its operating system as it is opensource and freely available. There is also a version of TinyOS specifically tailored to the
DSYS25 platform. See TinyOS for DSYS25 for details.
• http://www.cs.ucc.ie/misl/dsystems/HTML/dsys25.php
96.7 Papers
• Andre Barroso, Jonathan Benson, Tina Murphy, Utz Roedig, Cormac Sreenan, John Barton, Stephen Bellis, Brendan O'Flynn, and Kieran
Delaney. "The DSYS25 Sensor Platform", Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems
(SENSYS2004), Baltimore, USA, November 2004.
• Benson, JP and O?Donovan, T. and O?Sullivan, P. and Roedig, U. and Sreenan, C. and Barton, J. and Murphy, A. and O?Flynn, B.
"Car-park management using wireless sensor networks", Proceedings of the 31st IEEE Conference on Local Computer Networks,
Tampa, FL, pp588-595, 2006
• Barton, J. and Buckley, J. and O?Flynn, B. and O?Mathuna, SC and Benson, JP and O?Donovan, T. and Roedig, U. and Sreenan, C. "The
D-Systems Project-Wireless Sensor Networks for Car-Park Management", Proceedings of the 65th IEEE Vehicular Technology
Conference, Dublin, Ireland, pp170-173, 2007
See [1] for further publications
Back to Sensors
174
97 Eavesdropping
In wireless sensor network communications,an adversary can gain access to private information by monitoring transmissions between nodes. For
example in a healthcare scenario, the eavesdropper may deduce from the activity on the network, the state of health of the person using the system and
for instance the potential for there to be high value drugs in the home. Encrypting sensor node communications partly solves eavesdropping problems
but requires a robust key exchange and key distribution scheme. The scheme must be simple for the network owner to execute and feasible for the
limited sensor node hardware to implement (compromise between cost of hardware nodes and security). Robust routing protocols are one solution to
this problem. Another solution is multipath routing, which routes parts of a message over multiple disjoint paths and reassembles them at the
destination. Efficient discovery of the best disjoint paths to use for such an operation is certainly a research challenge
175
98 ECG
Electrocardiogram
176
99 Eco: Ultra-Wearable and Expandable Wireless Sensor Platform
Eco is a system developed at the University of California Irvine and describe here[1]. It is a self-contained, ultra-wearable and expandable wireless
sensor platform under 1cm3 and is only 15% the volume of Mica2DOT [2]. The Eco system claims (2006) to be possibly the world?s smallest form factor
for a wireless sensor node without sacrificing expandability and performance. It measures only 13mm × 10mm × 8mm = 1040mm3 including a battery
and weighs under 2grams. The applications it is being tested give merit to its practicality, namely infant monitoring and perfroming arts (dancing)
monitoring.
Eco Sensor
Perfroming Arts
Infant Monitoring
For the perfroming arts application, the base station converts the data stream into MIDI format [3] before sending to a host computer. This adds a whole
new dimension to dance performance and empowers the dancers by extending their control to the stage and props. In this application, the Eco platform
enables one to implement a truly wearable wireless body sensor network (BSN) and collects data from multiple types of sensing devices using the same
expansion port. Also, Eco?s high bandwidth realizes fast collection of a non-trivial amount of data from those sensing devices. In addition, the
WiFi-equipped data aggregator contributes to achieving high scalability, which is essential for group dance performance.
For the infant monitoring application, the Eco platform to monitors spontaneous movement of pre-term infants. One way to help infants grow in weight
and bone strength is to apply assisted exercise, although it must be closely monitored to ensure the infants are not adversely assisted. Therefore,
doctors need monitoring methods that are non-invasive and unobtrusive. Eco?s small form factor and light weight enable infants to wear sensor nodes
on their limbs so that doctors can monitor their movement in real-time in a cost effective way without the inconvenience of wired sensors or inaccuracy
of vision based techniques.
Back to Useability
177
178
100 Ember
179
101 Ember
Ember Corporation has developed the EmberNet embedded networking software and development tools. EmberNet is a low frequency self-organizing,
self-healing wireless embedded networking platform supporting the forthcoming ZigBee network standard. It produces networks that are reliable,
flexible, secure, and easy to use. EmberNet contains support for mesh-, star-, and hybrid-networks. It can be used to monitor, manage and secure
everything from bridges to office buildings to truckloads of frozen fish.
http://www.bb-elec.com/press_releases/images/ember/110-0019-002_rgb72.jpg
Ember [1]
Ember's Semiconductor products enable developers to choose between the EM250, a highly-integrated and cost-optimized System-on-Chip, and the
EM260, a ZigBee? network co-processor that can be paired with almost any microcontroller. The EM2xx family minimizes external components and
provides multiple RF connection options for easy use with or without external PAs.
101.2 EM250
Sensing:
None
I/O:
Radios:
CPU:
Storage:
• Seventeen GPIO pins with alternate functions
• Two Serial Controllers with DMA
♦ SC1: I2C master, SPI master + UART
♦ SC2: I2C master, SPI master/slave
• Integrated 2.4Ghz, IEEE 802.15.4 compliant transceiver:
♦ Robust RX Filtering which allows co-existance with IEEE 802.11g and
Bluetooth devices
♦ -99dBm RX sensitivity (1% PER, 20byte packet)
♦ +3dBm nominal output power
♦ Increased radio performance mode (boost mode) gives
• 100dBm sensitivity and +5dBm transmit power
• Integrated VCO and loop filter
• Integrated IEEE 802.15.4 PHY and lower MAC with DMA
• 16-bit XAP2b microprocessor
• Integrated memory:
♦ 128kB of Flash
♦ 5kB of SRAM
• Configurable memory protection scheme
101.3 EM260
Sensing: None
I/O:
Radios:
• Seventeen GPIO pins with alternate functions
♦ Two Serial Controllers with DMA
♦ SC1: I2C master, SPI master + UART
♦ SC2: I2C master, SPI master/slave
• Integrated 2.4Ghz, IEEE 802.15.4-compliant transceiver:
♦ Robust RX Filtering which allows co-existance with IEEE 802.11g and Bluetooth
devices
♦ -99dBm RX sensitivity (1% PER, 20byte packet)
♦ +3dBm nominal output power
♦ Increased radio performance mode (boost mode) gives - 100dBm sensitivity and
+5dBm transmit power
♦ Integrated VCO and loop filter
♦ Secondary TX-only RF port for applications requiring external PA
• Integrated IEEE 802.15.4 PHY and lower MAC
180
CPU:
Storage:
• User Defined
• Dependant on CPU choice
101.4 Application
• Industrial Automation
• Advanced Metering Infrastructure
• Integrated Home Automation
101.5 Power
The EM250/260's low operating and sleep currents may provide up to years of battery life depending on the application. EmberZNet PRO 3.1
networking provides ease of incorporating short activity cycles. The integrated voltage regulator supports a wide range of operating voltages that allows
hardware to be optimized for use with lithium-ion or alkaline batteries, without additional circuitry.
101.6 Software
• EmberZNet PRO is a complete ZigBee protocol software package containing all the elements required for robust and reliable mesh
networking applications on Ember?s silicon platforms.
• Official Ember Website
• Design of an IEEE 802.15.4-Compliant, EmberNet?-Ready or EmberZNet?-Ready Communication Module Using the EM2420 Radio
Frequency Transceiver
101.8 Papers
• N. Aakvaag, M. Mathiesen, and G. Thonet, ?Timing and power issues in wireless sensor networks - an industrial test case?, in Proc.
ICPPW, 2005, pp. 419?426.
• Egan, D. (2005), ?The Emergence of ZigBee in Building Automation and Industrial Control,? IEEE Computing & Control Engineering
Journal, Vol. 16, No. 2, April/May 2005, pp. 14-19.
Back to Sensors
181
102 EndoSure
http://www.cardiomems.com/
182
103 EnOcean
103.1 EnOcean
EnOcean is a technology supplier of self-powered modules (transmitters, receivers, transceivers, energy converter) to companies (e.g. Siemens,
Distech Controls, Zumtobel, Omnio, Osram, Wieland Electric, Peha, Thermokon, Wago, Herga), which develop and manufacture products used in
building automation (light, shading, hvac), industrial automation, and automotive industry (replacement of the conventional battery in tyre pressure
sensors).
Sensing:
I/O:
General-purpose modules to which the user can connect a variety of different sensors
Via general purpose I/O module
Radio(s)
• Frequency: 868.3 MHz (STM 110) or 315.0 MHz (STM 110C)
• Data rate / Modulation: 25 kbps / ASK
• Transmission power / Transmission range: max. 10mW / 300 m free field
• One-way and bidirectional communication
Radios:
CPU:
Storage:
Module dependant. See EnOcean for further details.
None
103.3 Application
Applications for EnOcean include industrial, Building and Home automation. See http://www.enocean.com/en/solutions/
103.4 Power
EnOcean uses properitary power harvesting techniques. The company?s products (such as sensors and radio switches) have no battery and are
engineered to operate maintenance-free. Early switches from the company used piezo generators, but these have been replaced with electromagnetic
energy sources to reduce the operating pressure (7 newtons), and increase the service life to 50,000 operations.
103.5 Software
EnOcean wireless modules always come with software. The aim in every case is easy use of the technology without special knowledge. Specific
software for tranceiver modules includes:
• Official EnOcean Website
• EnOcean from Wikipedia
103.7 Papers
See EnOcean for 'White papers'.
Back to Sensors
183
104 European Reimbursement Situation
In Europe the picture is very fragmented with small scale pilots being the order of the day and little or no reimbursement policies or joined up thinking
across countries. Recognizing this is in 2008 the European Commission published a report (1) aimed at identifying the underlying issues preventing the
adoption of telemedicine technologies and recommendations/actions to stimulate its adoption. It stated that ?Despite the potential of telemedicine, its
benefits and the technical maturity of the applications, the use of telemedicine services is still limited, and the market remains highly fragmented.
Although Member States have expressed their commitment to wider deployment of telemedicine, most telemedicine initiatives are no more than one-off,
small-scale projects that are not integrated into healthcare systems?. It goes on to say ??Patients' compliance is high and some healthcare authorities
have already acknowledged the need for these services. Yet, most telemonitoring services are still limited to the status of temporary projects without
clear prospects for wider use and proper integration into healthcare systems. Commitment by healthcare providers and concerted action between all
stakeholders are needed in order to ensure wider deployment of these types of services throughout the EU?.
The report identified three key areas that action needs to happen to stimulate the telemedicine market, these are;
• Building confidence in and acceptance of telemedicine services - The Commission has identified that there is a clear lack of ?hard data?
that quantifies the return on investment of telemedicine and telemonitoring. Lots of small scale pilots have been carried out; however no large
(national scale) pilot has been performed. The Commission?s position is that until this data becomes available, insurers are unlikely to enter
the fold and get creative around reimbursement policy. The Commission has agreed to publish a commonly agreed set of guidelines for
telemedicine across Europe as well as fund through the Competitiveness and Innovation Program framework (CIP), a large scale
telemonitoring pilot project.
• Bringing legal clarity - The lack of legal clarity ? in particular with regard to licensing, accreditation and registration of telemedicine services
and professionals, liability, reimbursement, jurisdiction ? is a major challenge for telemedicine. Cross border provision of telemedicine services
also require legal clarification with regard to privacy. Only a few Member States have clear legal frameworks enabling telemedicine. In some
Member States, for a medical act to be legally recognized as such, the physical presence of the patient and the health professional in the
same place is required; this is a clear obstacle to the use of telemedicine. Moreover, there are often limitations in law or administrative
practice on reimbursement of telemedicine services. The Commission has promised to support member states in the establishment of a
Common European platform to support legal issues and generate policy regarding data flow, ownership and accountability within the
European Union.
• Solving technical issues and facilitating market development ? The EU has identified issues such as Broadband deployment and device
interoperability as major issues needs to be addressed over the coming three years. It proposes to support industry and other efforts aimed at
overcoming these problems with particular emphasis on interoperability.
184
105 References
• 1. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the
Committee of the Regions. ?On telemedicine for the benefit of patients, healthcare systems and society?. Brussels, 4.11.2008 COM (2008)
689.
185
106 Examples of Systems Demonstrating Biocompatability
Imperial College London have developed a prototype system called the e-AR (ear-worn Activity Recognition) prototype (1) which involves an ear-worn
device that can be used to measure heart rate (with a moderate amount of motion), gait and posture. This device is not 'worn' in the sense of garments
and so has many potential advantages in terms of biocompatability.
There are some commercial Body sensor networks which have been certified for general use and have proven themselves over some years. We can
only draw implications as to their Biocompatability by their quoted success rates. Such systems include the
• CardioNET MCOT three lead ECG monitoring system (2), The Cardionetics C.Net 5000 24-Hour Ambulatory ECG Monitor (3) and others. As
requirements for certification are higher for implantable devices we are very interested in studying impantable devices and observing their long
term performance. Also on the market now are implantable devices that close the loop so to speak in terms of not only recording information
but also applying corrective action. Examples include the Medtronic Implantable Cardiovertor Defibrillator (4).
A very neat implantable system developed by Mark Allen at Georgia Institute of Technology and now commercialised by CardioMEMS (5) is their
implantable wireless EndoSure Aortic aneurysm of the abdomen (AAA) sensor. This tiny wireless and battery-less sensor fits in to the sac of an
Aneurism and transmits the pressure on the sac wall (which is an indication of likelihood to aneuritic rupture - from which there is only a 50% chance of
survival). It is a great example of a very small, low maintanance (none!), wireless system that will save your life.
Another example of biocompatability is the area of swallobable devices in the form of pills. One such example is the Given Imaging (27) 'PillCam'. This
capsule measures 11 mm x 26 mm and weighs less than 4 grams. It contains an imaging device and light-source on one-side and transmits images at a
rate of 2 images per second generating more than 50,000 pictures over an 8-hour period. The PillCam was initially cleared by the U.S. Food and Drug
Administration in 2001 and today is used by to detect and diagnose disorders of the small intestine. This includes Crohn?s disease, small bowel tumors,
malabsorption disorders (such as celiac disease), Gastro-intestinal injuries induced by extended NSAID (non steroidal anti-inflammatory drugs) use and
suspected Gastro-intestinal bleeding of the small bowel. The pill passes naturally with a bowel movement usually within 24 hours after the procedure.
Again, though there is little information available on the materials used here and we can draw our conclusions on biocompatability based on FDA
certification and over 700,000 successful procedures.
186
107 References
• 1. Benny Lo and Guang-Zhong Yang, "BODY SENSOR NETWORKS ? RESEARCH CHALLENGES AND OPPORTUNITIES" Institute of
Biomedical Engineering, Imperial College London, UK. http://www.doc.ic.ac.uk/~benlo/ubimon/BSN.pdf
• 2. http://www.cardionet.com/medical_02.htm
• 3. http://www.cardionetics.com/cnet5000.php
• 4. http://www.medtronic.com/your-health/tachycardia/device/our-implantable-defibrillators/virtuoso/index.htm
• 5. http://www.cardiomems.com/
• 6. http://www.givenimaging.com/en-us/Patients/Pages/pageSmallBowel.aspx
187
108 Examples of Systems Designed with Privacy & Security In Mind
Researchers at NASA Ames Research Centre and Stanford University have developed a personal monitoring system called Lifeguard (1) for use with
Astronauts. The requirements were for a rugged device capable of daily use in an extreme environment, whether due to pressure (hyperbaric,
hypobaric), vibration (shuttle launch), radiation (on-orbit), temperature and humidity (emergency workers) or other environmental factors. The system
was successfully used for measuring human physiology during ground-based centrifuge experiments, during mountain climbing, andunder-sea habitat
(NEEMO) used by NASA. In addition, the system was recently utilized to transmit vital signs from Licancabur volcano in Chile at 19,700ft, reflected off
an Inmarsat satellite, downlinked via France Telecom, seacross the Internet, and collaboratively view the physiological data in real-time at Stanford,
NASA, and off-site in Monterey, California.
A good example of privacy policy and security aspects in a wireless sensor networks is the ALARM-NET "Assisted-Living and Residential Monitoring
Network for pervasive, adaptive healthcare"(2) system developed by researchers at the University of Virginia- The system is intended for large scale
monitoring of pepole in residential care settings The system monitors both the Circadian Activities (i.e. the 24 hour behavioural patterns) of the people,
plus it also monitors physiological data (ECG, Pulse Ox etc). The hardware is based on MicaZ motes on Tiny OS. It integrates environmental and
physiological sensors in a scalable, heterogeneous architecture. The system features context-aware power management, dynamic privacy policies, and
data association. Communication is secured end-to-end to protect sensitive medical and operational information.
Harvard University rearchers have developed a system called CodeBlue (3) which was designed with reliability and robustness in mind. The reliability
versus overhead dilemma is nicely illustrated in this paper. For example, using multiple transmissions per packet increases robustness from 63% to 98%
which is good. However these data rates cause network saturation and reception ratios drop considerably. Bandwidth sharing among sensors was
specifically identified as being a key issue in ensuring reliable communications
188
109 References
• 1.Lifeguard- A Personal Physiological Monitor For Extreme Environments. National Center for Space Biological Technologies, Stanford
University http://lifeguard.stanford.edu/presentations/embc_lifeguard_paper_FINAL.pdf
• 2. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic. "ALARM-NET: Wireless Sensor Networks for
Assisted-Living and
• 3. Victor Shnayder, Borrong Chen, Konrad Lorincz, Thaddeus R. F. FulfordJones, and Matt Welsh. "Sensor Networks for Medical Care".
http://www.eecs.harvard.edu/~mdw/papers/codeblue-techrept05.pdf
189
110 Examples of Systems Designed with Security and Reliability In Mind
Researchers at NASA Ames Research Centre and Stanford University have developed a personal monitoring system called Lifeguard (1) for use with
Astronauts. The requirements were for a rugged device capable of daily use in an extreme environment, whether due to pressure (hyperbaric,
hypobaric), vibration (shuttle launch), radiation (on-orbit), temperature and humidity (emergency workers) or other environmental factors. The system
was successfully used for measuring human physiology during ground-based centrifuge experiments, during mountain climbing, and under-sea habitat
(NEEMO) used by NASA. In addition, the system was recently utilized to transmit vital signs from Licancabur volcano in Chile at 19,700ft, reflected off
an Inmarsat satellite, downlinked via France Telecom, across the Internet, and collaboratively view the physiological data in real-time at Stanford, NASA,
and off-site in Monterey, California.
A good example of privacy policy and security aspects in a wireless sensor networks is the ALARM-NET "Assisted-Living and Residential Monitoring
Network for pervasive, adaptive healthcare"(2) system developed by researchers at the University of Virginia- The system is intended for large scale
monitoring of pepole in residential care settings The system monitors both the Circadian Activities (i.e. the 24 hour behavioural patterns) of the people,
plus it also monitors physiological data (ECG, Pulse Ox etc). The hardware is based on MicaZ motes on Tiny OS. It integrates environmental and
physiological sensors in a scalable, heterogeneous architecture. The system features context-aware power management, dynamic privacy policies, and
data association. Communication is secured end-to-end to protect sensitive medical and operational information.
Harvard University rearchers have developed a system called CodeBlue (3) which was designed with reliability and robustness in mind. The reliability
versus overhead dilemma is nicely illustrated in this paper. For example, using multiple transmissions per packet increases robustness from 63% to 98%
which is good. However these data rates cause network saturation and reception ratios drop considerably. Bandwidth sharing among sensors was
specifically identified as being a key issue in ensuring reliable communications
190
111 References
• 1.Lifeguard- A Personal Physiological Monitor For Extreme Environments. National Center for Space Biological Technologies, Stanford
University http://lifeguard.stanford.edu/presentations/embc_lifeguard_paper_FINAL.pdf
• 2. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic. "ALARM-NET: Wireless Sensor Networks for
Assisted-Living and
• 3. Victor Shnayder, Borrong Chen, Konrad Lorincz, Thaddeus R. F. FulfordJones, and Matt Welsh. "Sensor Networks for Medical Care".
http://www.eecs.harvard.edu/~mdw/papers/codeblue-techrept05.pdf
191
112 EyesIFXv1
112.1 EyesIFXv1
Sensing:
Light, Temperature
I/O:
Infineon TDA5250
Radios:
• Standard: ASK, FSK Modulation
• Band: 868 MHz ISM Band
• Data rate: 19,2 kbit/s
• Energy consumption: 36 mW (TX); 28 mW (RX)
Texas Instruments MSP430F149
CPU:
• Clock: 4 MHz
• Flash memory: 60 KB
• RAM: 2 KB
• Energy consumption: 6,7 mW (maximal)
STMicroelectronics ST M25P05
Storage:
• Flash: 512 Kbit
112.3 Application
112.4 Power
112.5 Software
112.7 Papers
192
113 EyesIFXv2
113.1 EyesIFXv2
EYES (EnergY-Efficient wireless Sensor) is a hardware and software platform that is easily reconfigurable and can be used to explore the potential of
collaborative sensors in selfhealing networks.
http://www.infineon.com/export/sites/default/media/products/dlpics/2006_1/cd.jpg
Sensing:
Light, Temperature
I/O:
Infineon TDA5250
Radios:
CPU:
• Standard: ASK, FSK Modulation
• Band: 868 MHz ISM Band
• Data rate: 19,2 kbit/s
• Energy consumption: 36 mW (TX); 28 mW (RX)
• MSP430F1611 CPU Datasheet/Users Guide
♦ 10Kbyte RAM, 40Kbyte Flash
♦ Up to 8Mhz *8 Channels of 12bit A/D
♦ Extremely low power in periods of inactivity
External memory: Atmel AT 45DB041B
Storage:
• 4 Mbit of Flash
113.3 Application
• Home automation systems
• Medical sensors
• Security&alarm systems
• Building automation solutions
• Industrial monitoring systems
• Aircraft safety monitoring system
• Robotics
113.4 Power
The EyesIFXv2 runs off a 2500 mAh power supply.
113.5 Software
• TinyOS Support
• The development environment is open source and based around Linux, but can be run on Windows using a Linux emulator.
• EYES, Energy efficient sensor networks
• EYES ? New Solution for Energy-Efficient Wireless Sensor Networking
• Telecommunication networks group
• http://www.snm.ethz.ch/Projects/EyesIFXv21
• http://doc.utwente.nl/41407/
113.7 Papers
193
114 Finland
114.1 Reimbursement Model in Finland
Home monitoring and internet applications are seen as most promising growth areas at present. Successful trials have been identified as a critical
success factor for market development. Users are currently not expected to be charged for telehealth services as they are mainly still at development
stage and are thus supplied through pilot and trial schemes. Home telehealth services would presumably follow the normal practice in Finland, with a
growing but still relatively small user co-payment requirement under the mainly publicly-funded and publicly-provided healthcare system.
194
115 FireFly
115.1 FireFly
FireFly is a small form factor wireless sensor network platform and is capable of data acquisition, processing and multi-hop mesh communication. It has
a battery lifetime of 1.5-2 yrs depending on the application.
http://www.ece.cmu.edu/firefly/images/firefly_nodes.jpg
On-board sensing:
Sensing:
• Light
• Motion (PIR)
• Audio
• Temperature
• Acceleration (Dual Axis)
(note: A lower cost version of the board is available without sensing abilities called FireFly Jr.)
I/O:
Radios:
Interfaces to computer via USB
CC2420 IEEE 802.15.4 transceiver
Atmel Atemga32L 8-bit MCU
CPU:
• 32KB ROM
• 2KB RAM
Storage: SD flash card slot for data acquisition and storage
115.3 Applications
The FireFly can be customized to suit user-specific applications non-limiting to:
• Utility monitoring
• Surveillance
• Location tracking
• Voice communication
• Coal mine monitoring
115.4 Power
Runs on a 'AA' form factor capable of operating the FireFly for a period of 1.5-2 years depending on the application.
115.5 Software
FireFly runs the Nano-RK operating system.
• FireFly Homepage
115.7 Papers
• Anthony Rowe, Rahul Mangharam and Raj Rajkumar, "RT-Link: A Global Time-Synchronized Link Protocol for Sensor Networks"
Elsevier Ad hoc Networks, Special Issue on Energy efficient design in wireless ad hoc and sensor networks, 2007.
• Anthony Rowe, Rahul Mangharam, Raj Rajkumar, "FireFly: A Time Synchronized Real-Time Sensor Networking Platform," Wireless Ad
Hoc Networking: Personal-Area, Local-Area, and the Sensory-Area Networks, CRC Press Book Chapter, November 2006
• Anthony Rowe, Zane Starr and Raj Rajkumar, "Using Micro-Climate Sensing to Enhance RF Localization in Assisted Living
Environments" IEEE Systems, Man and Cybernetics, Montreal Canada 2007.
Back to Sensors
195
116 FitSense BodyLAN
The Pacer? is an accurate activity-monitoring device that can determine a user?s step count, distance traveled and caloric burn. The Pacer sensor
determines this through a patented accelerometer and methodology of recognizing each foot strike and foot contact time on the ground of the user. It?s
through this method that measurements of true calories burned are determined.
The Pacer? is easy to use and transfers its time stamped data automatically. It clips on to a user?s shoe using an attached elastic Rungee? cord (an
ankle mount version is available as well). A user then need only enter their weight into their BodyLAN? Feedback Device (e.g. FS-1 Watch) and they are
ready to go. The Pacer? is over 90% accurate out of the box, but can be adjusted through its calibration mode if a more precise distance measurement
is required. The Pacer? transmits information to the BodyLAN? system on a user defined time interval. In operational mode, the Pacer? continuously
timestamps every step with distance, step count, caloric burn, speed, cadence and battery power.
FitSense Body Lan System
196
117 Fleck
117.1 FleckTM 3
The Fleck series of nodes have been developed at CSIRO ICT Centre, and have primarily been used for Agriculture and Environmental applications. To
date, there have been 3 Flecks developed, each as an update of the previous version.
http://www.ict.csiro.au/images/Autonomous/FleckCircuitBoard.JPG
The Fleck3 shown here improves upon its two predecessors by introducing new and improved radio functions, memory upgrades, better power
characteristics and a smaller form factor.
Sensing:
• Built in temperature and light sensors
• Flecks may also make use a an expansion board for extra sensing abilities non-limiting to:
♦ Inertial sensor with triaxial accelerometer, gyroscope and magnetometer
♦ Strain guage
♦ Humidity
• 3 LEDs
• Screw terminals for:
♦ 4 digital I/O, interrupt, counters, PWM gen
♦ 2 analog inputs
• All flecks can be fitted with expansion boards, which are also stackable
I/O:
Expansion Interface
• 2 x 20 way connectors
• Robust 4 x hole mounting
Datamate connector
• 2 x RS232
Radios:
Nordic 905 (nRF905) radio transceiver
Atmel Atmega 128 processor
CPU:
• 512kbyte program flash
• 4kbyte RAM
*1 Megabyte external memory (upgradeable to 4Mb)
Storage:
• The Flecks may facilitate more storage by using an expansion board that supports SD/MMC
cards
117.3 Applications
• Environmental Monitoring
• Water Monitoring
• Agriculture
• Energy monitoring and control
117.4 Power
Fleck3 has an improved power supply that supports:
• both rechargeable batteries and super capacitors
• overcharge protection of rechargeable batteries
The power supply operates down to 1.3V, allowing it to scavenge almost all energy from a pair of AA rechargeable batteries. Supplemental to the
rechargeable batteries, the Fleck contains solar power charger circuitry allows the Fleck to harness the energy from the sun for a longer life span.
197
117.5 Software
• TinyOS
• Fleck Operating System (FOS) - a cooperative thread-based operating system for the Fleck wireless sensor module
• Wireless Sensor and Actuator Network: two-year progress report
• Keeping check with flecks
• CSIRO ICT Centre - the developers of fleck
• The Sensor Network Museumtm - Flecktm series
• The Powercom Group - The group looking after the commericial release of fleck
117.7 Papers
• P. Corke, R. Peterson, and D. Rus. Localization and navigation assisted by cooperating networked sensors and robots. Int. J. Robotics
Research, 24(9):771?786, Oct. 2005.
• Z.Butler, P. Corke, R. Peterson, and D. Rus. From robots to animals: Virtual fences for controlling cattle. Int. J. Robotics Research,
25(5-6):485?508, may 2006.
• G.Bishop-Hurley, D. Swain, D. Anderson, and P. Corke. What constitutes a reliable cue to stop animal movement? In 59th Annual Meeting.
Soc. Rangelands Management, Feb. 2006.
• Y. Guo, G. Poulton, P. Corke, G. Bishop-Hurley, T. Wark, and D. Swain. Analysis and modelling of live-stock behaviour using wireless sensor
devices. In G. J. Bishop-Hurley, editor, Proc of the Spatial Grazing Behaviour Workshop, pages 39?50, J.M. Rendel Laboratory,
Rockhampton, jun 2006. CSIRO.
Back to Sensors
198
118 Floor Vibration-based fall detectors
Floor vibration solutions falls into two categories. One type is based on the use of acoustic analysis of the sounds made when a person falls a contacts a
solid object or surface such a floor. However there are limitations to the technique especially with composite surfaces such a concrete with carpet
overlay for example.
The second type uses conversion of the mechanical energy that is generated when a person impacts an object/surface such as a floor it is converted
into electrical energy using piezoelectric sensors. The selectivity of the sensors is based on the hypothesis that the vibration signature of the floor
generated by a fall is significantly different from those generated by normal daily activities such as walking etc. and secondly the vibration signature of a
human fall is significantly different from those of falling objects. The Medical Automation Research Centre (MARC) at the University of Virginia have
demonstrated a sensor based on this approach. In controlled experiments conducted to test this fall detector they attained 100%true positives and 0%
false alarms with a detection range of 20 feet.
118.1 References
[1]
199
119 Further detail on Healthcare Reimbursement in the USA
Home healthcare reimbursements were first authorised in the Balanced Budget Act of 1997. From this it follows that Medicare (US healthcare provider)
does reimburse some telemedicine applications but ?this reimbursement only covers geographic areas with a shortage of health professionals,? and is
only for ?teleconsultations provided in real time and does not make provision for store-and-forward consultations, in which information is gathered and
stored for a physician to evaluate at a later time". This was improved upon in 2000 with the Benefits Improvement and Protection Act which in essence
increased the scope of eligible medical practices and eliminated the need for ?telepresenter? which was part of the 1997 act. The assumption here has
been that the service is being provided to a Healthcare Professional Shortage Area (HPSA) and the service is aimed at rural areas where clinical
support is scarce. In July 2008 the Medicare Improvements for Patients and Providers Act (1) whose function was to further increase the scope of the
Medicare coverage (including mental health and dialysis centers), passed both houses of Congress but on July 15th was vetoed by President Bush.
However later that day, the Congress overrode the veto and the bill finally passed in to law. The law formally went in to effect on January 2009.
The Medicare Telehealth Enhancement Act of 2008 (2) removes current geographic restrictions on the provision of such services. It also adds to the
types of facilities authorized to participate in the telehealth program. It also directs the Secretary of Health and Human Services to encourage and
facilitate the adoption of state reciprocity agreements for practitioner licensure in order to expedite the provision of telehealth services across state lines.
The latest Major Action: 6/2/2008: on this bill is ?Referred to House subcommittee on Health.?
200
120 References
• 1. Kinsella A. Home Telehealthcare: Process, Policy, & Procedure Sunriver, OR: Information for Tomorrow, 2003
• 2. http://www.americantelemed.org/files/public/policy/LegislativeAlert_29May2008.pdf
201
121 GaitRite
The GAITRite system from CIR System Inc is the most popular method for the objective measurement of Gait parameters in a clinical setting. The
GAITRite system automates the measurement of the temporal (timing) and spatial (distance) parameters of Gait via an electronic walkway connected to
the serial port of a PC. The standard GAITRite system comprises of six sensor pads encapsulated in mat which can be rolled up. The mat has an area
of 61cm x 366cm. The mat has 13824 sensors spaced at 1.27 cm apart. As the patient walks across the walkway the system captures data with
respective to each footstep and calculates both temporal and spatial parameters for the walk.
Temporal
Spatial
Step Time
Step
Gait Cycle
Stride Length
Ambulation Time
Step/Extremity Ratio
Velocity
Toe In/ Toe Out
Mean Normalised Velocity H-H Base of Support
Single Support
Distance
Double Support
Stance Time
Swing Time
121.1 References
202
122 GE QuietCare system
GE QuietCare System
GE QuietCare Motion Sensors
The GE QuietCare System (1) launched in 2009, is the first true commercially available 'proactive' healthcare solution. It uses multiple room sensors
loacted around teh home to pick up patterns of daily living. It aggregates the data via a central server that 'learns' the normal patterns of activity and
raises alerts/alarms when abnormal conditions are detected. The server also acts as the gateway for communications (external and internal).
203
123 References
• 1. http://www.gehealthcare.com/usen/telehealth/quietcare/proactive_eldercare_technology.html
204
124 Germany
124.1 Reimbursement Model in Germany
German law now allows direct contracts between providers and statutory health insurances. Telemonitoring services for patients with heart failure or
diabetes have been implemented by many health insurances. (Note: Presently the German health system is financed by a levy of about 14% on all
employment income and shared equally among employer and employee - for statutory health insurance). These services are free of charge for patients
insured by the health insurance offering the service. GPs get reimbursed for payments for telephone or similar contacts after initial examination in the
physician?s office and only GPs who are participating in the particular programme get reimbursed. For smart home solutions, no reimbursement
schemes are in place yet.
205
125 Glacsweb
125.1 Glacsweb
The Glacweb wireless platform is primarily focused towards measuring and observing climate change through its effects on glaciers. With this in mind,
the Glacsweb is designed to be capable of running for several years and gather data autonomously into a web accessible database.
http://envisense.org/glacsweb/tech/probe/probe2.jpg
Sensing:
I/O:
• 2 Tilt sensors
• Pressure sensor
• Strain gauge
• Resistance Bridge
• Temperature sensor
• 3D Accelerometer
• Humidity sensor
• Light reflection sensor
RS232 to connect to an anchor node
Single Channel Transceiver module -Radiometrix BiM1
Radios:
• Operating frequency - 173.250MHz
• Data rates up to 10kbps for standard module
• Usable range over 10km
• Feature-rich interface (RSSI, analogue and digital
baseband)
• Low power requirements
PIC18 based MCU
CPU:
• 128KB on-board memory
Storage: see above
125.3 Applications
The aim of the Glacsweb project is to monitor climate change through the effects on glaciers. The research aims to use technological advances to
understand what happens beneath glaciers and how they are affected by climate.
125.4 Power
The power was one of the main considerations when designing this node as once the node is placed in its intended location, it cannot be retrieved to
change or recharge the batteries. The Glacsweb's team have reduced the power consumption of the nodes by concentrating on the basic operating
protocols of each of the components.
• Power to the node is provided by three AA-sized Lithium batteries (2.25Ah).
• Each node has the ability to have all components asleep other than the Real time clock.
125.5 Software
Unknown
• Glacsweb Homepage
• Sensor networks for glaciers: Glacsweb
• Pervasive Systems Centre (PSC)
• Dr Kirk Martinez - one of the principle investigators in the project
125.7 Papers
206
• Hart, J. K., Rose, K. C., Martinez, K. and Ong, R. (2009). Subglacial clast behaviour and its implication for till fabric development: new
results derived from wireless subglacial probe experiments. Quaternary Science Reviews.
• Hart, J. K., Rose, K. C. and Martinez, K. (in press). Seasonal Changes in Basal Conditions at Briksdalsbreen Norway: The
Winter-Spring Transition. Boreas.
• Rose K. C. and Hart, J. K. (2008). Subglacial comminution in the deforming bed: inferences from SEM analysis,Sedimentary Geology,
203, pp87-97.
• Elsaify, A., Padhy, P., Martinez, K. and Zou, G. (2007) GWMAC- A TDMA Based MAC Protocol for a Glacial Sensor Network. In
Proceedings of 4th ACM PE-WASUN 2007, Chania, Crete Island, Greece.
Back to Sensors
207
126 Global Efforts Concerning Useability
Globally there are some wearable body sensor network systems appearing such as the Vivometrics Lifeshirt (1) which is a shirt garment designed to be
worn in the home during sleep. It is designed to measure vitals during sleep and provide indications of Sleep Apnea
The FitSense BodyLAN (2) system is a wearable system that is used to determine a user?s step count, distance traveled and caloric burn. The system
features the ActiHealth Actiped sensor which is a simple sensor for monitoring foot activity (walking , jogging etc). It is very practical in that it simply clips
on to the shoe.
The Nike and Ipod Rock and Run (3) system is also a great example of interaction between technology and personal area and well being. Also great
example of technology being pervasive i.e. in obtrusive. Very practical and acceptable.
A Spanish company Keruve (4) has come out with a GPS device, which can be worn like a bracelet, to keep a tab on Alzheimer's patients. The system
is made up of a special bracelet and a PSP like handheld device that indicates the location of the person wearing the bracelet. The bracelet is water
resistant and can only be taken off using a special tool.
Vibering (5) is a very neat system aimed at helping the hearing impaired. According to Yanko Design (5), "Vibering" is an ingenious way to help the deaf,
by fashionably housing a sound detection and identification system to be worn as a pair of rings and a wristwatch. The rings are to be worn on both
hands and are the ears that not only listen for sounds emanating from behind, they also determine distance, position and vibrate according to source.
The wristwatch aspect, identifies the sound wave and present this info to the wearer in an easy to read display. The watch is programmed to listen for
certain key phrases from humans like "Excuse Me..", your name being called and any number of car noises including the most important one, a car's
horn. This device concept could not just be a major life enhancer for the deaf, it would most certainly save lives."
Researchers in Harvard and MIT are developing the iShoe (6), which is a wearable shoe insole used to detect balance problems before a catastrophic
fall occurs and results in a bad break such as a hip fracture.
As an example of how pervasive these body sensor networks can be, a quite interesting and certainly very 'useable' device from Philips has recently
been reported in the New Scientist (7), and has been touted as "The Underpants that Could Save your Life". This device being developed is an
underpants that can monitor blood pressure continously using Pulse Wave Velocity instead of a normal cuff. It has been long known that Pulse wave
velocity is very closely related to blood pressure.
126.1 References
• 1. http://www.vivometrics.com/sleep/index.php
• 2. http://www.actihealth.com/
• 3. http://www.apple.com/ipod/nike/run.html
• 4. http://keruve.com
• 5. www.yankodesign.com/2008/06/03/stereo-listening-rings-two-rings-to-rule-them-all
• 6. http://web.mit.edu/newsoffice/2008/i-shoe-0716.html
• 7. http://www.newscientist.com/article/dn13929-invention-bloodpressuresensing-underpants.html
208
127 HL7 Message
This is a typical HL7 message for a patient being admitted to hospital. It shows Patient ID, Event, Next of Kin information and other Admissions (ADT)
type information, such as the hospital number.
209
128 Hoarder Board
128.1 Hoarder Board aka Swiss Army Knife (SAK)
The main purpose of the Hoarder board is the collection and preprocessing of sensor data. It was designed as part of the Every Sign of Life project out
of MIT Media Laboratory.
http://web.media.mit.edu/%7Esylvan/classes/affect/hoarder.jpg
Sensing: Sensing via daughter board connector to include EKG, EMG, EEG, skin conductance, and temperature
• CompactFlash connector
• Serial port
♦ PC-compatible (5V inverted) serial port up to 115200 bps
• Programmer port
♦ initial microcontroller programming using standard programmer with passive adapter
♦ reprogramming when the serial programming function is not accessible
• MITHRIL port
♦ I2C and power sharing for MITHRIL wearable network
• Daughter board connector
• optional 2-color LED
I/O:
Radiometrix 2-way half duplex FM Radio module (BiM2)
Radios:
• 64kbps
PIC16F877 20-MHz microcontroller
CPU:
• 14KB Flash
• 368 RAM
• 256 EEPROM
• 5 MIPS
Storage: Compact flash up to 1GB
128.3 Applications
Personal health-monitoring
128.4 Power
Power supply
• 4 AAA rechargeable or alkaline batteries without power regulator
• optional 5V power regulator for other battery or power adapter configurations
• sharing power on MITHRIL network
128.5 Software
• Programmed using PICSTART Plus
• PIC Code -- written in CCS C; contains working code used in several projects
• PC Code -- applications to exchange data and reprogram board
(code may be downloaded via Hoarder Board homepage)
• Hoarder aka Swiss Army Knife (SAK) board homepage
• Quantifying the Relationship between Real Life Moment-to-Moment Activities and Physiological Signals
• The Media Lab
210
128.7 Papers
• Gerasimov, V. and Selker, T. and Bender, W., Sensing and effecting environment with extremity computing devices, Motorola Offspring,
vol 1, No.1, 2002
Back to Sensors
211
129 How Organisations Attempt to Influence Public Policy
212
130 Special Interest Groups and Non Governmental Organisations
Special interest groups are private-sector organizations whose members share common interests or positions on public policy and who pool their
resources to gain a more prominent voice in policy debates. There are literally thousands of such groups representing almost every conceivable interest.
Some organizations have a long history of working toward a general goal, while others are formed temporarily to advocate for or against a specific policy
proposal.
Prominent examples of special interest groups are those that advocate for environmental protection, benefits for senior citizens, protection for minority
groups and free trade policies. Organisions concerned with policy relating to telehealth activities include the American Telemedicine Acssociation (ATA)
(1). In terms of the advancement of issues relating to ageing and technology, the Centre for Ageing Services and Technology (CAST)(2) is very
prominent. The Continua Alliance (3) is an example of an organisation that are working on common issues regarding telemonitoring devices
interoperability.
It should also be borne in mind that just as there are certain groups in support of particular policy, there may also be counter groups against the same
policy. So just because a technology for example seems to make sense to certain groups, doesnt mean to say that the opinion is unanimous and that
lobbying for policy change is straightforward. A good example of this is the Privacy debate currently happening in the RFID world. See the Privacy &
Security CAPSIL particular section Not Everyone is Enthusiastic About this Technology.
213
131 Public Policy Research Organisations
These organizations, sometimes called "think tanks," conduct original research, publish books and articles and prepare position papers on topics related
to public policy. Their experts often come from induatry but hold academic posts also. Their published works often are cited by others to support their
own positions. A example in Ireland is the Economics and Social Research Institue (ESRI) (4). The stated mission of the ESRI is ..."The ESRI produces
high-quality research that contributes to understanding economic and social change and that informs public policymaking and civil society in Ireland and
throughout the European Union".
Some prominent think tanks with a long history of contributing to US public policy debate include the Brookings Institution, the Heritage Foundation, the
Cato Institute, the American Enterprise Institute, and the Center for Strategic and International Studies.
214
132 Trade Associations and Labour Unions
Trade associations are membership organizations that represent the interests of a particular industry or profession. They communicate the concerns of
their members to policymakers both in the legislative and executive branches. Just as important, they report back to their members about new policies,
rules and proposals so that the members are educated about what is required of them. Very strong examples in Europe include the fisheries and
agriculture organisations. In the RFID world organisations such as Global Standards One GS1 (5) and the Association for Automatic Identification and
Mobility (6) are notable Trade Associations.
Even though the primary purpose of labor unions is to represent their members in negotiations with employers, unions also play a significant role in
influencing public policy. In speaking for their members, their input is considered whenever trade, environmental, workplace safety, health care and
other key issues are debated. Unions are considered influential because of their ability to mobilize their members to vote and speak out. Unlike business
associations, which typically represent a relatively small number of companies, trade unions represent thousands or millions of workers, who are also
voters.
215
133 Individual Businesses
Private citizens and businesses commonly form coalitions or interest groups to make their voices heard within government, but many also take steps on
their own to influence government policy. These activities include writing to their elected officials about particular policies, writing letters to the editor of
their local newspaper and appearing at hearings and other public functions where policy is debated. Because businesses are affected to a great degree
by government policies, many corporations have established government relations offices to represent their interests in Washington, Brussels and other
centres of power.
216
134 References
• 1. http://www.atmeda.org
• 2. http://www.agingtech.org/index.aspx
• 3. http://www.continuaalliance.org/
• 4. http://www.esri.ie
• 5. http://www.gs1.org
• 6. http://www.aimglobal.org
217
135 IMOTE2
135.1 IMOTE2
The Intel Imote2 is a platform developed around the low power PXA271 XScale CPU. The stackable abiltity and range of expansion boards allow this
system to be customized for a specific application.
http://www.xbow.com/Products/Product_images/Wireless_images/Imote2_lrg.jpg
Sensing available via expansion boards include:
Sensing:
• 3-Axis Accelerometer
• Temperature
• Humidity
• Light Sensors
Application Specific:
I/O:
• On/Off slider switch
• Hard Reset Switch
• User programmable
switch
• Multi-color Status
Indicator LED
• USB host/client
• 3 UART
• 2 SPI
• I2C
• SDIO
• GPIOs
• I2S
• AC97
• Camera Chip
Interface
• JTAG
The board is stackable with a range of expansion boards
using its interface connectors. (21 + 31 pin connectors)
Radios:
CPU:
• Integrated 802.15.4 Radio - TI CC2420
• Integrated 2.4GHz Antenna
• Optional External SMA Connector
• Intel PXA271 XScale® Processor at 13 to
416MHz
♦ 256Kb SRAM
♦ 32MB Flash
♦ 32MB SDRAM
• Intel Wireless MMX DSP Coprocessor
Storage: See CPU
135.3 Applications
• Digital Image Processing
• Condition Based Maintenance
• Industrial Monitoring and Analysis
• Seismic and Vibration Monitoring
135.4 Power
Power can be supplied by a rechargeable battery board, connected via either side of the board or direct from via the mini-B USB connector which may
also be used to charge Li-Ion or Li-Poly batteries.
135.5 Software
The IMOTE2 is compatible with several operating systems including:
• TinyOS
• Linux
218
• SOS
• Imote2 Datasheet
• IMOTE2 - Crossbow
• Crossbow Imote2 Blog
• Intel Mote 2
• Imote2 Yahoo Group
135.7 Papers
• Spencer B. F., Jr., Rice A. J., Structural health monitoring utilizing Intel's Imote2 wireless sensor platform, Proc. SPIE, Wireless for
SHM II, Structural health monitoring utilizing Intel's Imote2 wireless sensor platform, Vol. 6529, 2007
• Nachman, L., Huang, J., Shahabdeen, J., Adler, R., Kling, R., IMOTE2: Serious Computation at the Edge, IWCMC International,
pp1118-1123, 2008
• M. Pallikonda Rajasekaran, S. Radhakrishnan, P. Subbaraj, Elderly Patient Monitoring System Using a Wireless Sensor Network,
Telemedicine and e-Health. January 2009, Vol. 15, No. 1: 73-79
Back to Sensors
219
136 Intel's HealthGuide
Intel HealthGuide
Intel HealthGuide
The Intel Health Guide (1) product is a newly available suite made up of two main components. Firstly there is a set top device that can be used with
approved devices to take vital measurements (it comes with a full list of approved and interoperable devices, including blood pressure, ECG, peak flow
etc) There is also the capability for video calls (Teleconsultations) with the clinician and to manually enter data (medication compliance, pain
management etc). The second part is a sophisticated clinician management application that allows the clinician to easily see which of his/her patients?
needs priority of care based on home based test results. This application allows the clinician to communicate with the patient, develop care plans,
assign extra caregivers, printout reports and include motivational input. All done over secure and reliable communications paths.
220
137 References
• 1. http://www.intel.com/healthcare/ps/healthguide/index.htm
221
138 Ireland
138.1 Reimbursement Model in Ireland
There is no national policy focusing on home telehealth. More generally, it is not clear where home telehealth would fit in the organisational structure of
the health care system given the current public-private mix and generally not very integrated system. These factors constitute significant barriers to the
development of home telehealth at this time, and there are no clear financial or other incentives to healthcare providers to provide such services at
present
222
139 IRIS
139.1 IRIS
The IRIS mote is a multifunction low power module used for wireless sensor networks. The mote has available to it a large array of sensor boards to
customize this mote for a particular application.
http://www.xbow.com/Products/Product_images/Wireless_images/IRIS_sml.jpg
Sensing available via expansion boards non-limiting to:
Sensing:
I/O:
Radios:
• Temperature
• Barometric
• Pressure
• Acceleration/Seismic
• Acoustic
• Magnetic
• 51 pin connector for connection to a sensor
board
♦ Supports Analog inputs, Digital I/O,
I2C, SPI and UART
Atmel RF230
Atmel Atmega 128L
CPU:
• 128K Flash
• 512K Measurement (Serial) Flash
• 8K RAM
• 4K EEPROM
Storage: See CPU
139.3 Applications
• Indoor building monitoring and security
• Acoustic, video, vibration and other high speed sensor data
• Large scale sensor networks incorporating 1000+ points
139.4 Power
IRIS is powered via 2x AA batteries.
139.5 Software
IRIS is supported by MoteWorks which is based on theTinyOs environment. (see more info here)
• IRIS Datasheet
• Crossbow - IRIS 2.4GHZ
• Powercast RF Energy Harvesting powers Crossbow IRIS Mote
• Crossbow Solutions blog - IRIS mote
139.7 Papers
• Maala, B., Challal, Y., Bouabdallah, A., HERO: Hierarchical kEy management pRotocol for heterOgeneous wireless sensor networks,
IFIP International Federation for Information Processing, Vol. 264, pp 125-136.
• Pallikonda Rajasekaran, M., Radhakrishnan, S., Subbaraj, P., Elderly Patient Monitoring System Using a Wireless Sensor Network,
Telemedicine and e-Health. January 2009, Vol. 15, No. 1: 73-79
223
Back to Sensors
224
140 IShoe
Erez Lieberman, a graduate student in the Harvard-MIT Division of Health Sciences and Technology who developed the technology as an intern at
NASA. Lieberman is now testing the iShoe technology in a small group of patients. The current model is equipped to diagnose balance problems, but
future versions could help correct such problems, by providing sensory stimulation to the feet when the wearer is off-kilter
Lieberman originally developed the technology to help NASA monitor balance problems in astronauts returning from space. However the team
recognised amuch wider application base, such as for elderly people at risk from hip fracture. The iShoe insole would measure and analyze the pressure
distribution of the patient's foot and report back to their doctor. The device could also be outfitted with an alarm that would alert family members when a
fall has occurred. The team are currently performing clinical evaluation of the device.
The Harvard/MIT iShoe prototype
225
141 Journals
141.1 Journals
• Psychology and Aging Published by the American Psychology Association
• Journal of Aging and Health The Journal of Aging and Health explores the complex and dynamic relationship between gerontology and health.
• Age and Ageing Age and Ageing is an international journal publishing refereed original articles and commissioned reviews on geriatric
medicine and gerontology. Its range includes research on ageing and clinical, epidemiological, and psychological aspects of later life.
• The Journal of Aging Studies features scholarly papers offering new interpretations that challenge existing theory and empirical work.
• Open Longevity Science is an Open Access online journal, which publishes research articles and letters in all areas of experimental and
clinical research in geriatric medicine and aging science.
• Abstracts in Social Gerontology includes bibliographic records covering essential areas related to social gerontology, including the psychology
of aging, elder abuse, society and the elderly, and other key areas of relevance to the discipline.
• Ageline produced by AARP, focuses exclusively on the population aged 50+ and issues of aging.
• Ageing and Society is an international, scholarly journal that aims to promote the understanding of human ageing, particularly from the social
and behavioural sciences and humanities.
141.2 Books
• Aging America and Transportation
226
142 Kiwok AB - BodyKom SeriesTM ECG - Kiwok AB
http://www.kiwok.se/index.php?lang=2
227
143 Lifeguard
The NASA Ames Research Center (Astrobionics) and Stanford University (National Center for Space Biological Technologies) are currently developing
a wearable physiological monitoring system for astronauts, called LifeGuard, that meets all of the above requirements and is also applicable to clinical,
home-health monitoring, first responder and military applications. It is comprised of physiological sensors (ECG/respiration electrode patch, pulse
oximeter, blood pressure monitor), a small wearable computer (termed CPOD, for Crew Physiological Observation Device) with internal sensors (activity
via 3-axis accelerometers and skin temperature), and a display station (Tablet PC).
Lifeguard
228
144 L-Node
144.1 L-Node (SOWNet Technologies platform)
The L-Nodes are the latest edition from SOWNet Technologies. It's predecessor, the T-Node was originally developed as a research platform whereas
the L-Node is designed to meet market requirements. The L-Node is tiny, approximately one third the size of the T-Node. Its rectangular shape and
edge connector allow for much smaller designs.
http://www.sownet.nl/images/stories/L-nodes.png
Sensing:
I/O:
868 MHz Radio Transceiver
Radios:
CPU:
• Increased power output from 5-10dBm (compared with T-Node)
• Close to double the range of T-Node
The microcontroller has been separated from the radio module, allowing SOWNet to select the best
microcontroller for the application.
Storage:
More information will be available soon
144.3 Application
L-Nodes can be used for and implemented in:
• Building management systems
• Micro-climate monitoring like Relative Humidity moni-toring
• Dynamic evacuation systems (DES)
• Seat monitoring in the public railway sector
• Fire systems
• Intruder systems
• Men guarding
• Access Control systems
• Temperature monitoring (HVAC systems)
144.4 Power
The main power drain on the T-Node is the radio, so nodes conserve power by turning off their radio and going into sleep mode whenever possible. The
L-Node can operate up to five times longer on the same batteries due to a more energy efficient microcontroller and radio which can operate on as little
as 1.8 V. This allows the L-Nodes to use almost the entire capacity of two standard 1.5 V batteries, whereas the T-Node would leave a relatively large
part unused.
144.5 Software
TinyOS
• See also T-Node
• SOWNet Technologies
• T-Node product sheet
144.7 Papers
229
145 Low Power Antenna Design
145.1 Introduction
A good antenna design is required to realize good range performance and be the right type for the application. It must be matched and tuned to the
transmitter and receiver. To get the best results, a designer should have an idea about how the antenna works, and what the important design
considerations are.
145.1.1 Basic Terminology
Wavelength - Important for determination of antenna length, this is the distance that the radio wave travels during one complete cycle of the wave. This
length is inversely proportional to the frequency and may be calculated by: wavelength in cm = 30,000 / frequency in MHz.
Ground-plane - A solid conductive area that is an important part of RF design techniques. These are usually used in transmitter and receiver circuits.
An example is where most of the traces will be routed on the topside of the board, and the bottom will be a mostly solid copper area. The ground-plane
helps to reduce stray reactants and radiation. Of course, the antenna line needs to run away from the ground-plane.
dB (decibel) - A logarithmic scale used to show power gain or loss in an RF circuit. +3 dB is twice the power, while -3 dB is one half. It takes 6 dB to
double or halve the radiating distance, due to the inverse square law.
145.2 The Basic Antenna
An antenna is a conducting wire that carries an alternating current. This current generates an electromagnetic field around the wire which varies with the
current through the wire. If another wire is placed nearby, of similar dimensions, the electromagnetic field will induce an electric current similar to the
original at a reduction. If the wire is relatively long, in terms of wavelength, it will radiate much of that field over long distances.
The simplest antenna is the ?whip? which is a quarter wavelength wire that stands above a ground-plane. This design is most commonly found within
vehicles for broadcast radio, CB and amateur radio and was discovered by a gentleman by the name of Guglilmo Marconi in the 1890's.
Universally, antennas require at least two connection points as with any other electronic component. The whip antenna has a connection to ground with
the completion of the circuit being formed though the electromagnetic field that exists between the whip and the ground-plane. Another consideration
when designing an antenna is the size of the ground-plane. In this case, it should be at least a quarter wavelength radial spread around the base of the
whip. To be made smaller would affect the performance and would require a longer whip to compensate.
145.3 Antenna Characteristics
Gain - Antenna gain can be thought of as how strongly the antenna radiates compared to a reference isotropic antenna where an isotropic antenna
radiates equally in all directions. An antenna that radiates poorly has low ?gain?. A dipole is similar to a whip, but the ground-plane is replaced with
another quarter-wave wire. Overall performance is about the same. An antenna that is 6 dB less than a dipole is -6 dB. This antenna would offer one
half the range, or distance, of the dipole. Compact antennas are often less efficient than a dipole, and therefore, tend to have negative gain.
Radiation Pattern - Antennas radiate in concentric circles centred on the antenna itself. For the whip antenna, the radiation pattern can be described as
omni-directional with a ?null? point at the end of the whip. This means that the signal will be at a minimum at the end.
230
A vertical whip will therefore be ideal for communication in any direction except straight up. When designing an antenna, it is important to know the
radiation pattern for the particular antenna being designed so as not to have a ?null? point in the desired direction of propagation.
Polarization - Orientation or polarization of the antenna is almost important. If two communicating antennas are inversely polarized, the signal reception
at each antenna would suffer greatly. Signals may also become either linearly or vertically polarized upon leaving the antenna and reflecting of metal
objects and the ground.
Impedance - Another important consideration is how well a transmitter can transfer power into an antenna. If the antenna tuning circuit on a transmitter
(or receiver) is designed for a 50 ohm load, the antenna should, of course, have an impedance near 50 ohms for best results. A whip over a flat
ground-plane has an impedance near 35 ohms, which is close enough. The impedance changes if the whip is mistuned or bent down, or if a hand or
other object is placed close to it. The impedance becomes lower as the antenna is bent closer to ground. When the whip is tilted 45 degrees, the
impedance is less than 20 ohms. When the whip is bent horizontal to one-tenth of a wavelength above ground, the impedance approaches 10 ohms.
The resulting impedance mismatch, a 5:1 ratio (VSWR) will contribute an additional loss of 2.6 dB.
145.4 Types of Antenna
• Printed Circuit Whip, or ?Stub?
• The Short Whip
• The Short PCB Stub
• The Spiral
• The Helical (Coil)
• ?Chip? Antenna
• The Loop
• Semi-Loop
• Modified Dipole Antenna
• The Slot
• The Patch
145.5 References
• Miron, Douglas B., Small Antenna Design, Communication Engineering Series, Newnes, 2006
• Smith, K., Antennas for Low Power Applications, RFM Corp
• Milligan, Thomas A., Modern Antenna Design, WileyBlackwell; 2nd Edition edition, 2005
231
146 Medtronic Implantable Cardiovertor Defibrillator
http://www.medtronic.com/your-health/tachycardia/device/our-implantable-defibrillators/virtuoso/index.htm
232
147 Memory
147.1 SD Cards
233
148 MICA2
148.1 MICA2
The MICA2 is a third generation mote module used for low power, wireless sensor networks. A large array of available sensor boards make this mote
customizable for various applications.
http://www.xbow.com/Products/Product_images/Wireless_images/MICA2_Lg.jpg
• Temperature
• Barometric
• Pressure
• Acoustic
• Magnetic
Sensing:
board
I2C, SPI and UART
I/O:
868-870; 902-928 Mhz Transceiver (CC1000)
Radios:
• Range up to 30m indoors
• Port available to connect an external radio
antenna
CPU:
• 128K Flash
• 4K SRAM
• 4K EEPROM
• up to 16 MIPS
Storage: See CPU
148.3 Applications
• Wireless sensor networks
• Security, Surveillance and Force Protection
• Environmnetal Monitoring
• Large scale wireless networks (1000+ nodes)
• Distributed computing platform
148.4 Power
• 1+ year battery life on AA batteries (Using sleep modes)
148.5 Software
MICA2 is supported by MoteWorks which is based on theTinyOs environment.
234
• MICA2 Datasheet
• Crossbow - MICA2
• The Sensor Network Museumtm - Mica2
• Mica2 Power Experiments
148.7 Papers
• Alippi, C. Vanini, G., Wireless sensor networks and radio localization: a metrological analysis of the MICA2 received signal strength
indicator, Local Computer Networks, 2004, Italy, pp 579-580.
• Ma, J.,Chen, Q., Zhang, D., Ni, LM., An Empirical Study of Radio Signal Strength in Sensor Networks Using MICA2 Nodes, Technical
Report, 2006.
• Zhou, G. He, T. Stankovic, J.A. Abdelzaher, T., RID: radio interference detection in wireless sensor networks, INFOCOM 2005, Vol. 2, pp
891-901.
Back to Sensors
235
149 MICAz
149.1 MICAz
The MICAz is a 2.4Ghz Mote used for enabling low-power, wireless sensor networks.
http://www.xbow.com/Products/Product_images/Wireless_images/MICAz_Lg.jpg
• Temperature
• Barometric
• Pressure
• Acoustic
• Magnetic
Sensing:
board
I2C, SPI and UART
I/O:
Radios:
TI CC2420
CPU:
• 128K Flash
• 4K SRAM
• 4K EEPROM
• up to 16 MIPS
Storage: See CPU
149.3 Applications
• Wireless sensor networks
• Security, Surveillance and Force Protection
• Environmnetal Monitoring
• Large scale wireless networks (1000+ nodes)
149.4 Power
• 1+ year battery life on AA batteries (Using sleep modes)
149.5 Software
MICAz is supported by MoteWorks which is based on theTinyOs environment.
• MICAz Datasheet
• Crossbow - MICAz
• The Sensor Network Museumtm - MicaZ
236
• Nano-RK - MicaZ Support
• An Experiment using MICAz Wireless Sensor Nodes (Youtube Video)
149.7 Papers
• Mache, J., Allick, C., Charnas, J., Hickman, A., Tyman, D., Sensor Network Lab Exercises Using TinyOS and MicaZ Motes, Proceedings
of the International Conference on Pervasive Systems & Computing, 2006.
• L. Uhsadel, Comparison of low-power public key cryptography on micaz 8-bit microcontroller, Master?s thesis, Ruhr-University
Bochum, Faculty of Electrical Engineering and Information Technology, 2007.
• Su, W., Alzagha, M., Channel Propagation Measurement and Simulation of MICAz mote, WSEAS Trans. on Computers, Issue 4, Vol. 7,
Apr 2008, pp.259-264.
• Werner-Allen, G.; Swieskowski, P.; Welsh, M., MoteLab: a wireless sensor network testbed, Information Processing in Sensor Networks,
2005. IPSN 2005. Fourth International Symposium on , vol., no., pp. 483-488, 15 April 2005
Back to Sensors
237
150 Microcontroller
150.1 Overview
The heart of any wireless sensor node is its microcontroller MCU. It provides the computational capability to the sensor, but it differs from standard
central processing units CPUs in its focus on self-sufficiency, power efficiency, and low cost. Two of the most commonly utilized MCUs are Atmel's 8-bit
range processors and Texas Instruments MSP 430 family of 16-bit RISC processors. Atmel's MCUs are based on a Harvard architecture -- programs
and data stored separately -- with a RISC instruction set. They have clock speeds up to 20 MHz with 20 MIPS. However, many applications run the
MCUs at lower speeds in order to achieve lower power consumption. They also feature full integration onto a single die of the Flash, EEPROM and
SRAM. The architecture is optimized for high level programming languages, especially C.
Atmel MCUs have been successfully used a wide on wireless sensor platforms including DSYS25, MICAz, BTnode, and Tyndall Mote. Atmel's range of
32 RISC MCU's have also been utilized in sensors. The AMAT91 which is based on the ARM7TDMI? processor core was used by University of
Edinburgh in the ProSpeckz sensor.
Texas Instrument's MSP430 CPU family uses a von Neumann architecture -- ccommon memory space in which both program instructions and data are
stored -- with various memory and peripheral configurations. The MSP430 is designed specifically for ultra-low-power applications, using a flexible
clocking system and wide variety of operating modes designed to reduce power consumption, thereby extending battery life. The current draw when in
sleep/power down mode is 0.1-?A, 0.8-?A in standby and 250-?A in operating mode when running at 3V. The MSP430 can operate down to 1.8V,
further improving a superior power specification. The MSP430 is used on the SHIMMER, Telos, BSN node and Tmote wireless sensor nodes among
others.
150.2 MCU's
• ARM
• Amtel
• Intel 8051
• MSP430
• PIC Microcontroller
• XScale
150.3 References
238
151 Minnesota
151.1 Minnesota Reimbursement Model
• The Medicaid agency recognizes physician consultations (medical and mental health) when provided using interactive video or
store-and-forward technology. Interactive video consultations may be billed when there is no physician present in the emergency room, if the
nursing staff requests a consultation from a physician in a hub site. Coverage is limited to three consultations per beneficiary per calendar
week.
• Payment is on a fee-for-service basis, suing the same payment rate as for covered services furnished in a conventional, face-to-face manner.
Payment is made at both the hub and spoke sites. No payment is made for transmission fees.
• Minnesota uses consultation CPT codes with the modifier "CT" for interactive video services and the modifier "WT" for consultations provided
through store-and-forward technology. Emergency room CPT codes are used with a "GT" modifier for interactive video consultations done
between emergency rooms
239
152 Modified Dipole Antenna
A dipole can be shortened somewhat by bending the wire or line back on itself, but not too close to itself. We built a version on a PCB, shown at right.
This antenna has almost the same performance as a full size dipole, but is more compact. The thickness and dielectric constant of the board will affect
the tuning, so the length may need to be adjusted.
This type of antenna is an attractive solution where space allows. However, a dipole should not be located close to a large metal area or groundplane.
The groundplane will become part of the antenna, and performance will suffer.
Like the normal dipole, the radiation pattern shows deep nulls and good gain. The impedance is a little lower, but still near 50 ohms. Like many of the
previous antennas, radiation from the face of the board is just as strong as from the long edge.
240
153 MyHeart Project
MyHeart aims at fighting Cardio Vascular Disease [1] by preventive lifestyle and early diagnosis. For the continuous measurement of vital signs
electronic systems and sensors were developed and embedded into functional cloths. In continuous monitoring Electro Cardiogram (ECG)[2]
applications alternatives to gel-based (wet) silver/silver-chloride electrodes were sought. Gel-based sensor pads exhibit much better performance than
rubber based or textile based pads with a much higher signal to noise ratio [3]. However the gel-based contacts are not as practical as dry contacts
particularly when a lot of motion and sweating may be involved. Textile body contacts are very troublesome due to them being adversely affected by
motion and general body posture. They suffer a decline in signal quality depending on the particular motion and how 'snug' the contact is with the body.
To improve this situation, tighter fitting garments are necessary - leading to discomfort and also making dress and undress more of an issue. The
MyHeart project was concerned with looking at this problem and suggesting approaches that could give all the comfort and practicality of textile based
sensors but with the strong signal robustness of gel-based systems.
MyHeart Vest showing sensors and pocket for processor
MyHeart Processor
The MyHeart project measures ECG, respiration and acceleration via on-body sesnors and electronics. The on-body processing of these signals is
applied to derive relevant features before storing or transmitting the data from the body. Moreover, analog-digital conversion of the sensor signals must
be done as close as possible to the sensor to optimise signal quality, ensure higher reliability and optimise transmission lines and bandwidth usage. The
on-body processing acquires, filters and process sensor data as well as store and forward processing results wirelessly (via Bluetooth) to the user
station.
MyHeart uses the electronic system placed into a side pocket of the garment. It must be removed before washing of the textile. This approach has the
advantage that the electronics does not need a waterproof seal, however it requires a connector to the garment-based sensors that is manageable by
an untrained user so to some extent it can be said that this solution has a ways to go to be more practical and useable
The MyHeart project concluded that much more work is needed to make textile wearable type body sensor networks more robust and reliable as the
artifacts of motion and interference are major science challenges still to be overcome.
241
154 Nike and Ipod Rock and Run
The Nike + IPod Rock and Run System is another example of a wearable body sesnor system that is geared at personal training, calories burned and
the motivation thereof. It consists of a simple sensor placed in to the insole of teh Nike training shoe. This communicates wirelessly with an Apple iPod
Nano device and records, distance, speed, calories etc. Personal music can be played and motivational messages are also included. Once finished teh
routine, the data can be downloaded on to an Apple Mac or PC and logged as part of a personal training history.
242
155 OFSETH - Optical Fibre Sesnors Embedded in to technical Textiles for
Healthcare monitoring
The OFSETH project is investigating the use of optical fibre technologies integrated to textiles for the purposes of measuring various vital signs such as
electrocardiography ECG, respiratory and pulse oxidation levels.
Optical Fibre stitched into bandage
A prototype of a wearable optical fibre based belt
The use of optical systems offer some advantages over electrical ones particularly in areas such as MRI patient monitoring and situations where the
patient is moving a lot. It has the advantage of being free from electrical/EMC interference and also will not cause any tissue self heating (an area that is
of some concern for electrical devices particularly where higher power levels are involved). The OFSETH project will particularly look at using Polymer
Optical Fibres (POF) over standard Silica based fibres, A major focus will be the wearability and comfort of the final prototype system.
Back to Useability
243
156 On-node Data Processing
When on-node sensor data processing is considered, the resource constraints of currently available low-power micro-processors have to be taken into
consideration. Examples of previous work in this area include:
• A two layered model combining a Gaussian Mixture Model and a Markov Model was investigated in MIThril 2003. However, only the feature
extraction and Gaussian class-conditional posterior code were implemented on the 200MIPS StrongArm processor of the [MIThril] system.
• An algorithm with low complexity was proposed in Sola et al. for ambulatory activity classification using a decision tree based technique to
classify activities including resting, lying down, walking and running. The algorithm was implemented on a fixed-point Philips LPC2106 30MHz
micro controller. The 32-bit ARM processor can be considered to be a relatively high power processor compared to micro controllers in typical
wireless sensors. In addition to that, decision tree classifiers can be computationally intensive as logic operations are expensive processes.
Re-training decision trees could also lead to a change in their structure, meaning that the node programming has to be changed.
• Several papers for on-line ECG detection, including: Akazawa et al, Park et al. (the wearable motherboard), Luprano et al. (Myheart
project),and So and Chan (real-time ambulatory cardiac monitoring)
• An on-node classifier for an ear worn sensor that uses a Bayesian classifier with Gaussian class conditional densities in Lo et al. .
• An on-node detector of heart rate and SPO2 in Wang et al. using a miniaturized ear-worn sensor.
244
157 Other Websites
• Smart Thinking Home of Guy Dewsbury's thoughts on the use of technology to help impaired and disabled people; person-centred design and
the home.
• The Telecare blog, also run by Guy Dewsbury, dealing with person centered Telecare.
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158 Particles
158.1 Particles
Particle Computer is a platform for rapid prototyping of Ubiquitous and Pervasive Computing environments. The platform consists of an array of
hardware components, software applications and libraries for the hardware and a set of development tools.
http://particle.teco.edu/devices/particle2_29.jpg
Within the Particle Computer System there are 4 classes of hardware boards:
• Core boards
• Additional boards
• Interconect boards
• Development boards
158.2 Core boards
Core Boards contain the basic operating functionality for a Particle needing only two boards to communicate effectively to form a small wireless network.
Functionality to these boards may be extended by interfacing to an additional board.
The current and upcoming core boards of the Particles family are as follows:
• Particles (low power RF communication)
• µPart (micro sender-only node)
• cPart (low-power RF)
• zPart (ZigBee)
Board:
Particles
µPart
cPart
zPart
Sensing: Ball Switch
2 LED, Speaker
Ball Switch
1 LED
2 LED,
Speaker
2 LED
I/O:
Although some boards come with some I/O functionality and sensing abilities. All boards may be
enhanced further by interfacing to an additional board to provide many I/O and sensing capabilities.
TR1001
Radios:
• Uses the
AwareCon
(ConCom)
protocol
PIC16F6720
CPU:
Integrated
Integrated
• 4k RAM
• 128k Flash
• 5 MIPS
• 869,869.85,(303.825,
914, 433) bands
• 19.2Kbit data rate
• ConCom protocol
Chipcon
CC2420
• 868,(915,315,433
) bands
• 76,8kbit or
19.2 kbit data
rate
• ConCom
protocol
CC1010(8051)
PIC12F675
• 64 RAM
• 1.8k Flash
• 1 MIPS
Storage: 512k DRAM
• 2k RAM
• 32k Flash
PIC16F672x
• 4k
RAM
• 14k
Flash
•5
MIPS
512k DRAM
158.4 Application
• Ad-Hoc (Sensor) Networks
• Wearable Computers
• Home Automation
• Ambient Intelligence Environments
246
158.5 Power
The Particle computer is battery driven and can attain a lifespan of up to several years.
158.6 Software
The particles may be programmed using MPLAB and come with a range of 'ready-to-use' applications. TECO also supply an additional range of
development tools and applications which can be found here.
• http://www.snm.ethz.ch/Projects/Particles
• http://www.smart-its.org/
• http://www.teco.edu/index2.html
158.8 Papers
Various publications and articles based on the particle computer may be found at: http://particle.teco.edu/publications/index.html
Back to Sensors
247
159 Patient and environment assessment
248
160 Overview
All patients who are targeted to receive home telehealth (even the likeliest candidates who have chronic diseases and are willing to change behaviors
with telehealth help) must be assessed more closely than they would be for usual home care services. Patients can't simply agree to use telehealth to
meet the challenges of changing their life-long routines. They must be assessed thoroughly for physical and cognitive capabilities for perfroming these
tasks correctly and effectively. It is necessary to assess patients' cognitive skills indicating their abilities to remember or perform certain tasks without
onsite coaching by a nurse or other caregiver; and the patients' physical capabilities such as their degrees or limitations of hearing and seeing must be
assessed as well if telehealth contact is to be successful.
An example asessment form is given by The Telemedicine Information Exchange "Patient Assessment as Appropriate for for Assignment to Home
Telehealth" (1).
249
Example of Patient Assessment Form (1)
As was seen in the CAPSIL on Mobile and Home Monitoring systems getting the home "right" is an important consideration in deciding whether home
telehealth will work well. The home has to be assessed by qualified personnel as being appropriate for telehealth (or made to be appropriate and safe).
Personnel doing the assessments will be looking at measuring distances between telehealth workstation, telephone, and usual patient location (easy
chair in living room, chair at kitchen table, and so on), and usual routine footpaths in the room or rooms typically used by the patient. In assessing and
then preparing the home for telehealth, it is crucial that no hazards be introduced as a result of setting up a telehealth system, and any changes that are
introduced to a patient's usual setting or routine path are made safe. This safeguarding activity may call for nurses duct-taping wires to the floor, using
brightly colored tape on rearranged furniture, and otherwise taking all steps to avoid affecting patients' safety by introducing home telehealth i.e
"tele-proofing" a room to ensure safety.
Therefore it should be noted Not all candidates will be suitable for telehealth solutions! and proper assessment should be performed beforehand.
250
161 References
• 1. http://tie.telemed.org/articles/Patient_Assessment.pdf
251
162 Performance Oriented Balance and Mobility Assessment (POMA)
One of the most commonly used assessment scales is Tinetti?s POMA. It is a simple clinical scale that grades performance on 14 balance items and
gait items. The POMA has been shown to correlate with the Berg Balance Scale. Raiche et al [1] found that a POMA score lower than 36 provides a falls
prediction of 70% sensitivity but only moderate specificity 52%.
162.1 References
1. M. Raiche, R. Hebert, F. Prince, and H. Corriveau, Screening Older Adults at Risk of Falling with the Tinetti balance scale, The Lancet, 356,
2000, pp 1001-1002.
252
163 PicoCricket
163.1 PicoCricket
PicoCricket is a commercial wireless mote which is aimed at children as a toy for making artistic creations with lights, sound, music and motion.
http://www.picocricket.com/originals/CricketImages/high-res-photos/cricket-kit.jpg
Sensing available via additional plug-in modules:
• Light Sensor
• Sound Sensor
• Touch Sensor
• Resistance Sensor
Sensing:
I/O:
Radios:
CPU:
None
Microchip PIC
Storage: Unknown
163.3 Applications
The PicoCricket is a toy for children to play and learn with and may also be used as an educational tool for schools.
163.4 Power
Runs on 3x standard AAA batteries.
163.5 Software
The PicoCricket runs on propriety software, available from the PicoCricket hompage. The software known as PicoBlocks runs on both Windows and
Mac OSx.
• PickoCricket Homepage
• LiFELONG KiNDERGARTEN
• Smart Design - PicoCricket
• PIE Playfully Inventing & Exploring with Digital & Other Stuff
163.7 Papers
• Rusk, N., Resnick, M., Berg, R., & Pezalla-Granlund, M., New Pathways into Robotics: Strategies for Broadening Participation, Journal
of Science Education and Technology, 2008.
• Resnick, M., Sowing the Seeds for a More Creative Society, Learning and Leading with Technology, 2007.
• Resnick, M., Computer as Paint Brush: Technology, Play, and the Creative Society, In Singer, D., Golikoff, R., and Hirsh-Pasek, K.
(eds.), Play = Learning: How play motivates and enhances children's cognitive and social-emotional growth. Oxford University Press. 2006
• Pezalla-Granlund, M., Rusk, N., Resnick, M., Berg, R., Rethinking Robotics: Approaches and Ideas, Association of Science-Technology
Centers conference workshop, 2005
Back to Sensors
253
164 Porcupine
164.1 Porcupine 2v5
The Porcupine is a small wearable sensory unit for logging motion data and doing low-level activity recognition. The project includes electronic
schematics for the hardware, software for the microcontroller and client-side software for hardware-interaction.
http://www.mis.informatik.tu-darmstadt.de/People/kristof/notes/porcupine1/pict001s.jpg
Main Board:
• 3D accelerometer (ADXL330)
Sensing:
Logging board:
• 9 tilt switches
• 2 light sensors
• Temperature sensor
• RT clock
Data to the board is uploaded via USB
I/O:
Also contains:
• LED
• Button
Radios: Chipcon cc2420 - accessible through a Zigbee Add-on board
MICROCHIP PIC18F4550-I/ML
CPU:
• 32KB Flash
• 2,048 RAM
• 256 EEPROM
• 12 MIPS
Storage: Up to 4Gb of memory via MMC Connector
164.3 Applications
http://lh4.ggpht.com/_i9D9NKmDXYQ/STQBBOLDzII/AAAAAAAAAE0/SquN4g1n4_Y/Porc25design.jpg
The Porupine being a wearable sensor is mainly used for activity recognition through users actions. It has been involved in a number of projects to date
to include:
• Actigraphy for Bipolar Patients
• Activities of Daily Living (ADLs)
• Daily Activities Annotation & Recognition
• Gardening Activities
• Long-Term Sleep Study
• Smart Electronic Dice
• Sports Training Activities
164.4 Power
The board contains charging circuitry and is designed to be worn 24/7 without having to recharge its 3.7v battery in between. According to the online
documentation, the Porcupine should log data non-stop for weeks with a minimum of user interaction.
164.5 Software
The CSS compiler is used to program the PIC.
• Porcupine
• Porupines (hosted by Lancaster University)
254
• Porcupine v1(hosted by TU Darmstadt)
• Porcupine2 - Sourceforge
164.7 Papers
• Berlin, E., Optimizing Wearable Sensors under Technical and Usability Constraints , TU Darmstadt, Bachelor Thesis August 6, 2007
• Aronsen, AK., Long-Term Fine-Grained Actigraphy, TU Darmstadt, Diploma Thesis September 3, 2007
(for further information dee here)
Back to Sensors
255
165 Power Supply
Due to safety concerns, batteries remain the main choice for power supplies for medical devices, especially for wearable sensors. Depends on the
power density requires, commonly used batteries includes: Alkaline, Nickel-cadmium, Lithium-ion, Lithium-ion Polymer, Silver-oxide, Zinc-air, etc.
Primary batteries, such as, zinc-air cell, often has high power density comparing to rechargeable batteries, such as Nickel-cadmium, but rechargeable
batteries are often chosen to minimise the cost and the affects on the environment.
To enable continuous sensing and prolong the lifetime of sensors, extensive research works have been conducted in power scavenging techniques.
Thermal power and human motion are two common approaches for power scavenging for wearable sensors.
Another approach to power up miniaturised sensors, especially for implantable sensors, is bio-fuel cell, where glucose are collected and used as the fuel
to power up the sensors.
256
166 Printed Circuit Whip, or ?Stub?
The whip can be made as a trace on a printed circuit board (PCB). This is very practical at frequencies over 800 MHz. At lower frequencies, a full size
whip may be too long, even when rapped around a few corners. The length of the whip should be 10 to 20% shorter than the calculation, depending on
the dielectric and the thickness of the board. In most cases, 15% shorter is close enough. If the unit is to be hand held, the antenna can be made a little
shorter, to compensate for the effect of the hand. At 916 MHz, a trace that is 2.25 inches (57 mm) long will provide a reasonable impedance when hand
effects are included. Keep the antenna trace away from other circuitry and ground, a quarter of an inch (6 mm), or more. Non-ground circuit traces may
be seen by the antenna as part of the ground system, and RF voltages can be induced on nearby traces. Our sample PCB Stub is shown in the drawing
at right. The overall size of the board and ground is not critical. The radiation pattern is omnidirectional, with a gain of -8 to -12 dBd, when the board is
horizontal. Polarization is horizontal. If the whip did not run parallel to ground, the gain would be higher, however, two sharp nulls would be present. If
the board were oriented vertically, with the antenna above the groundplane, the polarization would be vertical. The antenna would have an
omnidirectional pattern with -8 dBd of gain.
257
167 Privacy
major comcern. Radio Frequency Identification (RFID) has been leading the charge in the deployment of 'intelligent' network nodes being widely
networks treat privacy through security mechanisms i.e. encryption and data protection throughout the hardware and software layers. This section will
be more concerned with the 'softer' issues of privacy and look at fundamental human rights, protection of personal data and 'design for privacy'
guidelines for sensor systems.
258
168 Privacy and Ethics
networks treat privacy through security mechanisms i.e. encryption and data protection throughout the hardware and software layers. This section will
be more concerned with the 'softer' issues of privacy and the ethcial considerations involved. It will look at fundamental human rights, protection of
personal data and 'design for privacy' guidelines for sensor systems.
259
169 Privacy Concerns of Wireless Sensor Networks
The Privacy & Security CAPSIL has a detailed treatment of the privacy and security issues involved with wireless sensor networks. Follow this link
Privacy & Security.
260
170 Privacy, Security and Ethics
261
171 Overview
Home healthcare monitoring poses the fundamental problems of security and privacy balanced with safe and effective healthcare. In reality these can be
two opposing ideas as the more effective the home healthcare package, the more threat to privacy that exists. A major issue that comes up in this
context is that of trust. ?How do I know that others are not viewing my healthcare data without my explicit permission?? The Office for the Advancement
of Telemedicine (OAT) (1), identified consumer fears such as, ?the presence of outsiders or non-clinical persons in teleconsultations, such as
non-clinical technicians, camera people and schedulers located on either side of a telemedicine consultation or at the site of a service provider, either
physically or via the technology they support. Clinical personnel who may not be visible or observable by the patient may also be involved in a
teleconsultation. Patient information routinely stored electronically and/or physically at each site may not be protected by policies or procedures as
effectively as information used in on-site encounters.?
The use of the internet as a means of accessing and communicating has its problems and security is a very real concern. The Civic Research report on
Home Health Care Technology states, ?Currently, there are no standard protocols for protecting the security of email, telemetry, or electronic health
records. Further, firewalls and encryption, while they may slow down the process, are unlikely to deter someone motivated to access personal health
records. Wireless transmissions pose even greater concerns. Concerns regarding privacy, confidentiality, and security of health information have always
existed; however, the ease with which, the extent to which, and the context within which they may be breached are intensified with the electronic
exchange of information.? Patients must feel protected from ?nightmare scenarios,? which include accidentally transmitting medical information to the
wrong address or to someone masquerading (as a physician for example) and allowing hackers to break into medical information that they then
broadcast over the Internet.
Privacy concerns are more accentuated in the older people group as evidenced by a 2005 Pew Internet survey (2) which shows that, ?sixty-one percent
of those 65 and older say they are ?very concerned? about businesses and people they don?t know getting personal information about them or their
families, compared to forty-six percent of Americans between ages 18 and 29?.
171.1 Situation in Europe
In Europe there are a number of policies that deal with privacy and security. The particular ones relevant to telemedicine are as follows;
• Directive 95/46/EC ? ?On the protection of individuals with regard to the processing of personal data and on the free movement of such data"
• Directive 2000/31/EC (The ?e-Commerce Directive?) - The e-Commerce Directive defines rules for the provision of Information Society
Services both within and between member states.
• Directive 2002/58/EC ? ?Concerning the processing of personal data and the protection of privacy in the electronic communications sector?.
• Directive 2005/36/EC - Establishes the criteria for a set of regulated professions according to which qualifications obtained in one member
state are recognised by another.
171.2 Situation in the USA
In the USA the applicable law regarding healthcare privacy is called The Health Insurance Portability and Accountability Act (HIPAA). The act is roughly
broken in to two sections one of which protects health insurance coverage for workers and their families when they change or lose their jobs. The
second, known as the Administrative Simplification (AS) provisions, requires the establishment of national standards for electronic health care
transactions and national identifiers for providers, health insurance plans, and employers. The Administration Simplification provisions also address the
security and privacy of health data. The standards are meant to improve the efficiency and effectiveness of the nation's health care system by
encouraging the widespread use of electronic data interchange in the US health care system
An extensive treatment of the issues around Privacy, Security and Ethics are given in the Privacy & Security CAPSIL.
262
172 References
• 1. Kumekawa, Joanne K. (September 30, 2001) ?Health Information Privacy Protection: Crisis or Common Sense?" Online Journal of Issues
in Nursing. Vol. #6 No. #3, Manuscript 2 http://www.nursingworld.org/ojin/topic16/tpc16_2.htm
• 2. Fox, Susannah (October 5 2004) ?Digital Divisions.? A Pew Internet White Paper.
http://www.pewinternet.org/pdfs/PIP_Digital_Divisions_Oct_5_2005.pdf
263
173 Project STELLA - Stretchable Electronics for Large Area Applications
Direct skin contact of body sensor systems allows for full measurement capabilities, but wearing comfort requires that the monitor will not only be
flexible, but also of stretchable and soft-touch nature. In the STELLA project the development of such stretchable and soft-touch substrates, including
assembly with electronic devices on these substrates is proposed.
Processes will be developed to produce a stretchable conductive pattern that can be composed of printed wiring, discrete wiring, or a combination
thereof formed in a predetermined arrangement on a stretchable common base substrate. The soft-touch will be achieved by using base materials with a
different chemistry than what is used today for flex substrates.
Bendable Electronics Substrates
Crumple Wiring
Assembly methods for component mounting and interconnection, based on existing platform techniques, will have to be adapted.
The STELLA project which finishes in Jan 2010, promises to make breakthroughs in three distinct areas;
• 1.New stretchable substrates with stretchable conductor patterns
• 2.Assembly technology on stretchable substrates, based on lead-free reflow soldering
• 3.Integration methods for electronics in stretchable products
264
174 Publications
1. Jovanov, E., "Wireless Technology and System Integration in Body Area Networks for m Health Applications," Proc 27th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '05), Shanghai, China, Sept. 1-4, 2005, pp.
7158-7160
2. Jovanov, E., A. Milenkovic, C. Otto, P. de Groen, "A wireless body area network of intelligent motion sensors for computer assisted
physical rehabilitation", Journal of NeuroEngineering? and Rehabilitation, Vol. 2, No 6, 2005.
3. C. Xijun, Max Q.-H. Meng, and R. Hongliang, "Design of Sensor Node Platform for Wireless Biomedical Sensor Networks", Proc 27th
Annual International Conference of the IEEE Engineering in Medicine and Biology (EMBS ?05), Shanghai, China, Sept. 1-4, 2005, pp
4662-4665.
4. Hongliang, R, C. M. Q.-H. Meng, and X. Chen, "Physiological Information Acquisition through Wireless Biomedical Sensor Networks",
Proc IEEE International Conference on Information Acquisition, Hong Kong and Macau, China, Jun. 27-Jul. 3, 2005, pp. 483-488.
5. Lymberis, A., "Smart Wearables for Remote Health Monitoring, From Prevention to Rehabilitation: Current R&D, Future Challenges",
Proc 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, Birmingham, U.K, April
24-26, 2003, pp.272-275.
6. Otto, C., A. Milenkovi?, C. Sanders, E. Jovanov, "System Architecture of a Wireless Body Area Sensor Network for Ubiquitous Health
Monitoring", Journal of Mobile Multimedia, Vol. 1, No.4, 2006, pp. 307-326.
7. Lymberis, A., "Wearable Health Systems Applications: The Contribution of Information & Communications Technologies", Proc IEEE
27th Annual Conference Engineering in Medicine and Biology, Shanghai, China, Sept. 1-4, 2005, pp. 4149-4152.
8. Lo, B P.L., S. Thiemjarus, R. King and Guang-Zhong Yang, "Body Sensor Network - A Wireless Sensor Platform for Pervasive
Healthcare Monitoring", Adjunct Proc 3rd International Conference on Pervasive Computing, Munich, Germany, May 8-13, 2005, pp 77-80.
9. A. Stankovic, Q. Cao, T. Doan, L. Fang, Z. He, R. Ganti, S. Lin, S. Son, R. Stoleru, and A. Wood, "Wireless Sensor Networks for In-Home
Health-care: Potential and Challenges", In High Confidence Medical Device Software and Systems (HCMDSS) Workshop, Philadelphia,
PA, 2-3 June, 2005,
10. J. M. Eklund, T. R. Hansen, J. Sprinkle and S. Sastry, "Information Technology for Assisted Living at Home: building a wireless
infrastructure for assisted living", Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai,
China, September 1-4, 2005, pp. 3931-3934.
Back to Sensors
265
175 Radio Transceiver
175.1 Radio's
Wireless sensor network solutions for at-home health care and body worn sensing generally work in the ISM band. This of course generates an
immediate challenge as the technology must be reliable, robust and inexpensive and deployed into environments where there is potentially heavy
interference. One key issue researchers find is that performance figures generated in controlled environments bear little relationship to those
experienced in the real world.
There are a number of potential wireless technologies which could be used the deployment of WBSN. These include 802.15.4/Zigbee, Bluetooth, UWB
and 802.11. Each technology has it pro's and cons. Defining the requirements for any BSN deployment greatly impacts the choice of wireless
technology solution. Initial solutions will likely focus on the deployment of a small number of sensors typically between 1 and 6 sensors connected to a
aggregation device which communicates via WLAN protocol, WiMAX or mobile phone nextwork such as GSM, 3G etc. For the body worn sensors
802.15.4 and Bluetooth are de facto choices.
802.15.4 was designed specifically with sensors networks in mind. It has many desirable features which make is suitable for WBSN's e.g. low power etc.
However it has many disadvantages including short range due to its low power profile (not a issue for the BSN but problematic for communications with
an off body aggregation device), limited data rate (less than 250 kps) and its susceptibility to interference. Many of the medical applications being
developed using 802.15.4 are based around motes. Motes are low-power devices with a limited amount of computing power to relay measured data to a
data aggregator or gateway. These wireless mote based sensors offer the capability to capture continuous real-time data such as patient vital signs and
relay this back to nurses and physician workstations. Many of these projects are still in the design and deployment phase with initial designs yet to be
tested. One of the challenges which must be addressed is the issue of low power communications. The central hubs are likely to be 802.11 enable for
broadband connectivity, therefore interfacing low-power, ad-hoc sensor networking devices may be a problem. Another issue which must be addressed
is that wireless sensor networks ensure "best effort" delivery of data but do not provide transmission methods for critical data i.e. medical e.g. detection
of a patient fall. While Zigbee and Bluetooth focus many similar application areas they have very different network architectures and effective ranges.
Over the long term Bluetooth and 802.15.4 Zigbee may evolve to be complementary technologies.
175.1.1 Bluetooth
Bluetooth® is a low-cost, low-power, robust, short-range wireless communication protocol which was initially founded by Ericsson in 1994 to replace
traditional mobile phone and computer cables with wireless links. It operates in the license free 2.4 GHz ISM band. Bluetooth® uses 79 1MHz channels
to transmit data. Interference between other ISM band devices (802.11 and 802.15.4 devices) and other Bluetooth® piconets is minimized using
frequency hopping spread spectrum (FHSS), where the carrier is rapidly switched (hops) among the 79 available channels. The frequency hopping
sequence is controlled by the master device within the piconet. Other Bluetooth® interference reduction techniques include adaptive power control,
[Channel Quality Driven Data Rate] (CQDDR) and Adaptive Frequency Hopping(AFH).
It was first developed as cable replacement between mobile phones, headsets, PDAs, laptops etc, but since then it has evolved to solve more general
applications in the Personal Area Network (PAN) domain. The Bluetooth stack is quite complicated, giving it a rather large footprint, which means that it
cannot be used in the most processing-power and memory constrained devices.
The Bluetooth core system consists of an RF transceiver, baseband, and protocol stack. The system is usually implemented partly in hardware and
partly in software running on a microprocessor. The partitioning can be configured in different ways depending on the application. From solutions where
the radio, protocol stack and application runs on a single chip to solutions where there is a separate radio chip, a processor running the lower layers of
the stack and yet another processor running the upper layers of the stack and the application. Extensive documentation and analysis of Bluetooth® and
its applications can be accessed from the Bluetooth® SIG's website
175.1.2 802.15.4/Zigbee
IEEE 802.15.4 is a specification of a low-power air interface, and the accompanying MAC protocol. 802.15.4 is a CSMA/CA MAC -based system, with a
total of 27 channels specified in the frequency bands of 2.4 GHz, 902-928 MHz, and 868.3 MHz. Three different over-the-air data rates can be allocated:
16 data channels with a data rate of 250 kb/s, 10 channels with a data rate of 40 kb/s and 1 channel with a data rate of 20 kb/s. Such a network can
choose one of the 27 channels depending on availability, congestion state, and data rate of each channel. It is optimized for short range
communications (typically 30-50 meters), low data throughput with a 30ms network join time and supports a flexible topologies, i.e. star or peer-to-peer
topologies. It also supports very large numbers of nodes, a single 802.15.4 network can accommodate up to 216 devices, which are assigned during the
association procedure. It is designed to maximize energy efficiency at the physical and MAC layers. The duty cycle of communications in an 802.15.4
network is around 1 percent, resulting in very low average power consumption for static and dynamic environments. However, it is also up to higher
protocol layers to observe the low duty cycle. Most power saving mechanisms in 802.15.4 are based on beacon-enabled mode.
The 802.15.4 defines only one third of the total number of primitives used within Bluetooth and is therefore suitable for simple devices with limited
memory and computational capacity. Two different types of devices are defined: a full function device (FFD) and a reduced function device (RFD). An
FFD can talk to RFDs and FFDs while an RFD can only talk to an FFD
IEEE 802.15.4 determines which radio hardware to use and Zigbee determines the content of messages transmitted by each network node. [ZigBee]
(on top of 802.15.4) ensures interoperability by defining higher network layers and application interfaces. The simple complexity, low-cost, low-power
features of 802.15.4 are intended to enable broad deployment of wireless networks able to run for years on standard batteries, for a typical monitoring
application.
802.15.4 is part of the IEEE's 802.15 wireless personal-area network specification activities. It uses a simple (28K byte) packet-based radio protocol
aimed at very low-cost, battery-operated sensors that can intercommunicate and send low-bandwidth data to a central receiving station.
266
802.15.4/ZigBee is built on the IEEE 802.15.4 standard and specifies the MAC and PHY (physical) layers. The "ZigBee" comes from higher-layer
enhancements in development by a multi-vendor consortium called the Zigbee Alliance. For example, 802.15.4 specifies 128-bit AES encryption, while
ZigBee specifies but how to handle encryption key exchange. 802.15.4/ZigBee networks are designed to run in the unlicensed frequencies, including the
2.4-GHz band in the U.S.
IEEE 802.15.4/ZigBee is intended for uses such as control of lights, security alarms, motion sensors, thermostats and smoke detectors, environmental
monitoring etc. There are plans for Zigbee integration with residential gateways that merge traffic onto a broadband Internet connection. Zigbee has
specific advantages over other short range protocols such as 802.11 and 802.15.4 for WSN applications as devices based on these protocols use too
much power and the protocols are too complex (and thus more expensive) to be embedded in devices on very large scales [1, 2]. Unfortunately, the
ZigBee Alliance has its protocol closed at the moment; additionally, it adds another protocol between the device and the global IP-based network.
175.1.3 Ultra Wide Band
Impulse-radio-based UWB technology has a number of inherent properties that are well suited to sensor network applications. In particular, impulse
radio-based UWB systems have potentially low complexity and low cost; noise-like signals that are resistant to severe multi-path and jamming; very
good time domain resolution, allowing for location and tracking applications
The low complexity and low cost of impulse radio UWB systems arise from the essentially baseband nature of the signal transmission. Unlike
conventional radio systems, the UWB transmitter produces a very short time domain pulse that is able to propagate without the need for an additional
radio frequency(RF)mixing stage. The RF mixing stage takes a baseband signal and "injects" a carrier frequency or translates the signal to a frequency
that has desirable propagation characteristics. The very wideband nature of the UWB signal means it spans frequencies commonly used as carrier
frequencies. Since high burst data rates are achievable with UWB systems, a sensor employing UWB can transfer its data payload quickly and spend
much of the rest of the time "asleep" or in a low-power state [3].
175.2 Radio Chipsets
175.2.1 Bluetooth
• Chipcon CC1000
• Chipcon CC1020
• Nordic nRF2401
• Xemics XE1205
175.2.2 802.15.4
• TI CC2420
175.3 References
• Wexler, J., " Zigbee Vendor Group to Wireless Enable Facilities Monitoring ",
http://www.networkworld.com/newsletters/wireless/2003/0825wireless1.html, Sept. 2006.
• Zigbee Alliance, " Information and Resources " http://www.zigbee.org/en/resources/#WhitePapers, Sept 2006
• Opperman, L. Stoica, A. Rabbachin, Z. Shelby and J. Haapola, "UWB Wireless Sensor Networks: UWEN - A Practical Example", IEEE Radio
Communications, December, 2004, pp. 27-32.
267
176 Reliability
A major challenge with wireless sensor networks in general to date is how to ensure that comminications is reliable and robust. Issues such as number
of network nodes, battery power, communications protocol used, physical motion of the nodes, operating environment, network to human interface etc,
all pose a challenge for thses networks and variations in any of these parameters can mean that the network can fail completely. For body sensing
where healthcare monitoring is crucial, obviously the newtork can not be so suceptible to failure and must be very robust. This section will detail some
examples of systems that have demonstrated these principles.
268
177 Reliability and Stability
A major challenge with wireless sensor networks in general to date is how to ensure that communications is reliable and robust. Issues such as number
of network nodes, battery power, communications protocol used, physical motion of the nodes, operating environment, network to human interface etc,
all pose a challenge for these networks and variations in any of these parameters can mean that the network can fail completely. For body sensing
where healthcare monitoring is crucial, obviously the network can not be so susceptible to failure and must be very robust. This section will detail some
examples of systems that have demonstrated these principles.
269
178 Reports
178.1 P. van der Stok, WASP Deliverable D1.2, State of the art, Information society technologies,
March 2007
Abstract This document describes the State of the Art in Wireless sensor networks as perceived by the WASP consortium. After a description of the
advance in the domains of the work-packages, a section continues with describing the State of the Art within the consortium for the given workpackage.
The document concludes with the extensions to the State of the Art the consortium will peruse in the coming one year period.
Back to Sensors
270
179 RF and Body effects
Another issue to be considered here is the body effects on RF signals i.e. how the body effects RF signals. Higgins has shown that antenna design is
crucial isn body sensor networks and teh increas in dielectric constant from having a human body around actually works in the designers favour in that
smaller antenna can be used.(physically small antennas though do have their drawbacks also!)
271
180 RF Emissions and Interference Aspects
Most Mote systems use the unlicenced Industrial Scientific and Medical band ISM Band. For wireless communications, the FCC Federal
Communications Commission has allocated the frequency range of 402-405MHz for medical implant communication services (MICS), and the frequency
ranges of 608-614MHz, 1395-1400MHz and 1427-1432MHz for medical telemetry. It is crucial that interference does not occur and this will become
more of an issue as wireless sensor networks grow in popularity.
Note: A major issue in the roll out of RFID - Radio Frequency-identification devices in the retail/logistic sectors has been the wireless interference form
sources as benign as flourescent lighting and cordless phones, causing false readings, loss of data packets and corrupt packets being received.
Wireless sensor networks are expected to comply with strict EMC (radiating and interfering) guidelines and these are detailed by two main IEC
documents.
• The first is concerned with radiated emissions and is titled - IEC/EN 61000-4-3: Electromagnetic compatibility (EMC). Part 4: Testing and
measurement techniques. Section 3: Radiated, radio-frequency, electromagnetic field immunity test? (1). This standard is applicable to the
immunity requirements of electrical and electronic equipment to radiated electromagnetic energy. It establishes test levels and the required
test procedures. The test method documented here describes a consistent method to assess the immunity of equipment or systems against
immunity to RF electromagnetic fields from any source. Particular considerations are devoted to the protection against radio-frequency
emissions from digital radiotelephones and other RF emitting devices.
• The second standard deals with electromagnetic immunity and is titled - ?IEC EN 55024: Information technology equipment, immunity
characteristics, limits and methods of measurement? (2). This standard establishes uniform requirements for the electromagnetic immunity of
information technology equipment. It defines the immunity test requirements for equipment defined in the scope in relation to continuous and
transient, conducted and radiated disturbances, including electrostatic discharges (ESD).
The mechanism that most wireless networks (802.11 and 802.15.4) use to allow multiple users to share the same frequency band is called Carrier
Sense Multiple Access with Collission Aviodance (CSMA/CA). The use of CSMA/CA obliges network designers to take into account interference issues
among near operating networks or from electronic and telecommunication devices working in the surrounding environment. In particular, the signals
radiated by such sources may occupy the channel assigned to a wireless network, which is forced to wait until the end of interference, causing delays or
even worse, packet losses.
Bertocco et al (3) perfromed detailed interference and EMC measurements of CSMA/CA based networks (802.15.4)and found no significant effects for
radiating interference for the wireless sensor network. Secondly they performed tests around the effects of in-channel Additive White Gaussian Noise
(AWGN) interference. They concluded that network operation is successful until the received interference power does not overtake the CCA (Clear
Channel Access) threshold. They also concluded that the overall performance of the wireless network is completely a function of interfering signal level
and CCA level (and not dependent on the ratio of interfering signal to original signal level).
There is a surprising lack of information dealing with RF emmissions/immunity of body sensor networks in particular although much work has been done
on wireless sensor PHY and MAC layers. For medical grade applications in which multiple network nodes may be deployed in to for example hospital
environments, much further work focussing on the issues or reliability and RF compatability is needed. Most prototype solutions currently look at issues
almost in isolation i.e. reliability, packet loss, latency, power management etc but not the system as a whole. Therefore more work is needed on end to
end solutions and the analysis of overall operating parameters.
272
181 References
• 1. http://webstore.iec.ch/Webstore/webstore.nsf/0/0532BCCE36AEEEA3C125742C000B2E45
• 2. http://www.bsigroup.com/en/Shop/Publication-Detail/?pid=000000000030162817
• 3. M. Bertocco1, G. Gamba1, A. Sona1, S. Vitturi. "Performance Measurements of CSMA/CA-Based Wireless Sensor Networks for Industrial
Applications". IMTC 2007 ? IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland, May 1?3, 2007.
http://www.dei.unipd.it/~giogamba/pdf/IMTC_2007.pdf
273
182 Risk factors
182.1 Catogories
Falls are unpinned by a number of different risk factors. These are categorized into four domains:
182.1.1 Biological Risk Factors
Biological factors which are non-modifiable relate to the human such as age, gender and race. These are also associated with the changes due to
ageing such as the decline of physical, cognitive and affective capacities and the co-morbidity associated with chronic illnesses. The interaction of
biological factors with behavioural and environmental risks increases the risk of falling. For example the loss of muscle strength leads to a loss of
function and to a higher level of frailty, which increases the risk of falling due to some environmental hazards.
182.1.2 Behavioural Risk Factors
These factors are associated with a person actions, emotions or daily choices. For example risky behaviour such as the intake of multiple medications,
excess alcohol use and sedentary behaviour can be modified through strategic interventions for behavioural change.
182.1.3 Environmental Risk Factors
Environmental factors relate the interplay of the individuals with their environment; these include home hazards and hazardous features in the public
environment. Home hazards include narrow steps, slippery surfaces on stairs, loose rugs and poor lighting. In the public environs issues such as
cracked or uneven foot paths, poor lighting, short pedestrian light sequences, slippery surfaces etc.
182.1.4 Socioeconomic Risk Factors
These relate primarily to the person?s social conditions and the economic status of the individual which has a direct impact on their access to
healthcare. Factors include: low income, low education, poor housing, lack of social interaction, limited access to social health care services particularly
in remote rural areas and a lack of community resources.
182.2 Intrinsic and Extrinsic Factors
The Michigan Public Health Institute in 2004 report entitled ?Comprehensive Fall Prevention For Community-Dwelling Older Adults? divide falls risks
into intrinsic ?within oneself? and extrinsic factors ?outside influences?.
One of their conclusions was that a lack of knowledge among health professionals? with respect to assessment tools, treatment interventions and
effective communication and compliance skills is also an extrinsic risk factor for older adult falls. In successful multifactorial intervention programmes the
following specific components are common (against a background of the general diagnosis and management of causes and recognised risk factors): A
1. strength and balance training 2. home hazard assessment and intervention 3. vision assessment and referral 4. medication review with
modification/withdrawal. Of these 4 factors technology has a potential role to play in 1 and 2 in terms of reducing the risk factor to significantly lower
levels.
182.2.1 References
[1] Michigan Public Health Institute, Comprehensive Fall Prevention For Community-Dwelling Older Adults, 2004, Available
http://www.mphi.org/files/Fall%20Prevention%20for%20Community%20Dwelling%20Older%20Adults%202004.pdf
274
183 Semi-Loop
This is an unusual design that looks like a loop, but requires no direct grounding. It is comparable to a loop in performance, and can be adjusted to
present a non-reactive load. This antenna uses a trace that runs all the way around the edge of a small PCB. The far (open) end is capacitively coupled,
through the board, back to the transmitter end of the antenna. The antenna is resonated by varying the length of the short overlapping line. Tuning is not
very critical. Hand effects will improve the impedance, with little effect on tuning. Polarization is parallel to the PCB, and the pattern is omni-directional.
As with any other designs, this antenna should not run too close to ground. For this design, the transmitter and other circuitry, including battery, should
be grouped around the center of the board, leaving the antenna in the clear. The circumference of the board needs to be well under one-quarter
wavelength. We have had good results with a circumference of about 0.15 wavelength, and a line width of 1 to 1.5 mm, when used in the 400 MHz
region. If the design is used on a thinner board, the 5 mm overlap will need to be shortened.
275
184 Sensing
276
185 Sensing
Sensors fall into two realms based on the frequency of data coming through; these are a continuous feature stream and a discrete state description. An
example of a continuous feature stream is the data coming from real-time monitoring of an ECG signal; whereas a discrete state stream might be the
data coming from a motion sensor. In general, sensors can be also classified in terms of physical, chemical and biosensors. Sensors for WBSN
generally fall into the physical sensing classification.
185.1 Physical Transducers
These sensors do not have to be directly exposed to the sample and are therefore more rugged and reliable in long term deployments. They are
generally low power and often low cost. They are the most suitable for scale up and for real-time data generation. Although they are not specific sensors
for chemical or biological species, they can provide general information about the environment and personal health. Examples include thermistors,
vibration sensors, accelerometers, photodetectors, pressure sensors, acoustic sensors, and non-contact conductivity/impedance. Low-cost spectral
information can be generated by combining selective light sources like LEDs (narrow band emitters) with photodetectors so that the spectral region is
associated with the particular absorbance band of a specific target [1]. An example is the use of red LEDs in pulse oximetry to determine the varying
concentration of oxygenated haemoglobin in real time. Important developments in this area include the emergence of new materials such as soft
polymer sensors and actuators (e.g. that are biocompatible and can mimic the function of muscles) [2]. These are increasingly being integrated into "lab
on a chip" devices to provide low-cost, low power methods for moving samples and reagents around microfluidic manifolds, and perform relatively
complex analytical measurements in a compact device [3]. They are also being integrated into textiles to generate wearables capable of sensing
movement and breathing. Related devices in this class also include ECG/EKG/EMG electrodes that provide real-time data on aspects of heart function
[4]. A particular focus for research in this field is on how to obtain good quality data from dry contact electrodes.
185.2 References
1. King-Tong, L., S. Baldwin, M. O'Toole, R. Shepherd, W. J. Yerazunis, S. Izuo, S. Ueyama and D. Diamond, "A low-cost optical sensing device
based on paired emitter-detector light emitting diodes", Anal. Chim. Acta., Vol. 557, No. 1-2, 2006, pp. 111-116.
2. Brady, S., K.T. Lau, W. Megill, G.G. Wallace and D. Diamond, "The Development and Characterisation of Conducting Polymeric-based
Sensing Devices", Synthetic Metals, Vol. 154, No. 1-3, 2005, pp. 25-28.
3. Causley, J., S. Stitzel, S. Brady, D. Diamond and G. Wallace, " Electrochemically-induced fluid movement using polypyrrole ", Synthetic
Metals, Vol. 151, No. 1, 2005, pp. 60-64.
4. Linz, T., C. Kallmeyer, R. Aschenbrenner, and H. Reichl, "Embroidering Electrical Interconnects with Conductive Yarn for the Integration of
Flexible Electronic Modules into Fabric", Proc 9th IEEE International Symposium on Wearable Computers (ISWC'05), Osaka, Japan, Oct
18-21, 2005, pp 86-91.
277
186 Sensor Fusion
Sensor fusion is the process of combining data provided by various sensors to better solve a problem than using the sources individually. Fusion can be
at different levels, including the data level, the feature level and the decision level.
The use of multiple sensors with information fusion has the following main advantages compared to single sensor systems Thomopoulos 90:
• Improved signal to noise ratio
• Enhanced robustness and reliability in the event of sensor failure
• Extended parameter coverage
• Integration of independent features and prior knowledge
• Increased dimensionality of the measurement
• Improved resolution, precision, confidence and hypothesis discrimination
• Reduce uncertainty
Fundamental issues to be addressed in building a data fusion system for a particular application include Hall and Llinas 977:
1. what algorithms or techniques are appropriate and optimal for a particular application;
2. what architecture should be used (i.e., where in the processing flow should data be fused);
3. how should the individual sensor data be processed to extract the maximum amount of information;
4. what accuracy can realistically be achieved by a data fusion process;
5. how can the fusion process be optimized in a dynamic sense;
6. how does the data collection environment (i.e., signal propagation, target characteristics, etc.) affect the processing;
7. under what conditions does multi sensor data fusion improve system operation?
In this page, sensor fusion at the following levels will be explained:
• Direct data fusion
• Feature level fusion
• Dimensionality reduction
• Feature selection
• Decision-level fusion
186.1 Direct Data Fusion
Sensor fusion at the data-level allows us in many cases to overcome some of the inherent limitations of single elements of the ensemble Yang 06.
Another use could be self calibration of some sensors in a sensor array. Techniques of direct data fusion include the following:
• Optimal Averaging of sensor data Xiao et al. 05
• Sensor Data Imputation:
♦ Multiple statistical imputation website
♦ The multiple imptation faq website
♦ A Data Imputation Model in Sensor Databases
♦ A Data Analyst's perspective to multivariate missing data problems
♦ The use of Gaussian Processes to predict missing data using correlation information.
♦ Source recovery at the data level:
♦ ICA: The original paper and a brief introduction . Also, ICA code in matlab and C
♦ Kernel ICA Bach and Jordan 03
♦ Non-linear ICA webpage
186.2 Feature Level Fusion
Fusion at the feature level involves the integration of feature sets corresponding to different sensors. These feature vectors are often fused to form joint
feature vectors from which the classification is made. The first feature towards feature level fusion is that of effective feature detection. Once features
are selected, the role of feature-level fusion is to establish boundaries in feature space and separate patterns belonging to different classes. Thus two
main issues are of importance: feature detection and the use of distance metrics for clustering. Feature Detection
In general, signal features can be classified into the following 3 categories:
• Time domain features describing waveform characteristics (slopes, amplitude values, maxima/minima and zero crossing rates) and statistics
(mean, standard deviation, energy, kurtosis, etc)
• Frequency domain features (periodic structures, Fourrier coefficients, spectral density)
• Hybrid features covering both time and frequency domains (Wavelet representations, Wigner-Ville distributions, etc...)
186.3 Distance Metrics and Clustering
Widely used distance metrics include the Mahalanobis distance and the Euclidean distance. Methods for distance-based clustering include the following:
• k-means clustering Kanungo et al. 02,MPIKmeans and Hamerly and Elkan 03
278
• ISODATA
• Agglomerative clustering
• Fuzzy c -means clustering
186.4 Dimensionality Reduction
The intrinsic dimensionality of a dataset is usually related to the number of independent variables that account for most variability within the data. Some
of the techniques that address dimensionality reduction are:
• SVD (Singular value decomposition)
• PCA
• Non-linear PCA, probabilistic PCA
• Multi-dimensional scaling (MDS), comparison of algorithms for MDS
• Locally linear embedding (LLE)
• Isometric mapping (Isomap)
• Gaussian Process latent variable models (GPLVM)
• Self Organising Maps (SOM)
186.5 Feature Selection
The aim of feature selection is to reduce the complexity of an induction system by eliminating redundant and irrelevant features. Advantages of feature
selection include reducing computational cost and storage as well as improving prediction accuracy. In machine learning, feature selection is normally
divided into two groups: wrapper and filter methods. Wrapper methods use the estimated accuracy of an induction algorithm to evaluate candidate
feature subsets. Filter methods, on the other hand, learn from data and operate independently of any induction algorithm.
Relevant links to feature selection include:
• NIPS 2003 Feature selection challenge, inclusing datasets and a comparison of algorithms
• JMLR Special issue on variable and feature selection
• RELIEF
• A video lecture on Feature selection (link)
• Analysis of relevance and redundancy in feature selection algorithms
• Margin based feature selection
186.6 Decision Level Fusion
Decision level fusion is generally based on a joint declaration of multiple single source results (or decisions) to achieve an improved classification or
event detection. At the decision level, prior knowledge and domain specific information can also be incorporated. Widely used methods for decision level
fusion include the following:
• Bayesian inference (Information theory, inference and learning algorithms, book by David Mackay)
• Classical inference; computing a joint probability given an assumed hypothesis usually using Maximum aposteriori (MAP) or maximum
likelihood decision rules.
• Dempster-Shafer's method
279
187 Sensor interfaces
There are main two types of sensor interfaces:Analog and Digital interfaces.
187.1 Analog Interface
Analog interfaces are commonly used in simple sensors, such as thermistor and light sensors. However, certain digital sensors, such as vision sensor,
may also provide analog outputs. To read analog sensor signals, an Analog-to-Digital Converter ADC is required. Some microcontrollers,such as the
MSP430 and the Atmel Atmega, have built in ADCs, and dedicated high precision ADCs are often used when the processors do not have built-in ADC,
such as the Intel IMOTE2.
187.2 Digital Interface
In general, there are three commonly used digital interfaces:
• Inter-Integrated Circuit Bus (I2C)
I2C, developed by Philips, is a half-duplex, synchronous and master-slave communication interface. To send/receive data, the master device generates
the clock signal, and the slave device read/transmitted the data bits in MSB first order. I2C interfaces can often be found in ultrasound range sensors,
humidity sensors, etc.
• Serial Peripheral Interface (SPI)
SPI, developed my Motorola, is fairly similar to the I2C in terms of data bit shifting, but instead of half-duplex, it enables full duplex communication and
uses a Chip Select signal to identify slave devices. With date rate up to 1Mbps, SPI is often used for interfacing complex chipsets, such as radio
transceivers and ADCs.
• Universal Asynchronous Receiver/Transmitter (UART)
UART (or Transistor-Transistor Logic (TTL) version of the RS232) is the asynchronous serial interface used by the serial ports of Personal
Computers(PCs). The asynchronous nature of UART simplifies the interface between devices where no clock signal is required. UART interfaces are
often used in more advanced sensors, such as SpO2.
280
188 SHIMMER
188.1 SHIMMER Wireless Sensor Platform
SHIMMER is a small sensor platform designed for wearable applications by Intel's Digital Health Group. The platform features an integrated 3-axis
accelerometer, large storage (micro SD card), and low-power standards based communication capabilities on the based board. It supports standalone
application as such as motion capture. Additional sensing capabilities can be added via extension boards which connect to the base platform via Hirose
20 position connector. The platform is fundamentally a radio agnostic platform supporting both the 802.15.4 and Bluetooth standards in a low-power
system architecture.
281
Sensing:
3-Axis Accelerometer using Freescale MMA7260Q 1.5/2/4/6g Micropower MEMs Accelerometer into
CPU A/D
I/O
• Reset button
I/O:
Expansion
• Hirose ST60 series 18 position rugged mobile style external Header for charging,
programming, and tethered sensor extensions (12 multi-purpose I/O connections).
• Hirose DF12 series 20 position internal Expansion header for internal sensor daughter
boards (14 Multi-purpose I/O connections)
802.15.4 Radio
Radios:
• Chipcon CC2420
• GigaAnt Rufa 4.1dBi Antenna
Class 2 BluetoothTM Radio
• WML-C46N CSR based design
CPU:
282
♦ Proven solution in medical Sensing applications
Storage: MicroSD slot (Up to 1 GB currently available)
188.3 Application
The goal of SHIMMER is to provide an extremely compact extensible platform for long-term wearable sensing in both connected and disconnected
settings using proven system building blocks. The design is realized using conventional module design and assembly technology to ensure repeatability
and economy.
188.3.1 Current applications
• The TRIL Centre is currently using SHIMMER to capture kinematic and physiological data during gait analysis.
• Harvard Medical have demonstrated the use of the use of the SHIMMER platform for home monitoring of people with Parkinson's Disease.
188.4 Power
• Design target is 10 days while sampling 6 channels at 50Hz w/250mAH battery.
• "Deep Sleep" shelf life is >1 year per battery spec
• Integrated Li-ion battery charger
• Ability to monitor and indicate power status
188.5 Software
• SHIMMER firmware is based on TinyOS. Sample code and applications can be found here: http://sourceforge.net/projects/shimmer/
• High level application support is available in the BioMOBIUS software environment.
• SHIMMER Hardware guide
• SHIMMER user pages (Requires subscription)
• SHIMMER is commercially available from Realtime Technologies
188.7 Papers
• Baker, Chris R.; Armijo, Kenneth; Belka, Simon; Benhabib, Merwan; Bhargava, Vikas; Burkhart, Nathan; Minassians, Artin Der; Dervisoglu,
Gunes; Gutnik, Lilia; Haick, M. Brent; Ho, Christine; Koplow, Mike; Mangold, Jennifer; Robinson, Stefanie; Rosa, Matt; Schwartz, Miclas;
Sims, Christo; Stoffregen, Hanns; Waterbury, Andrew; Leland, Eli S.; Pering, Trevor; Wright, Paul K., Wireless Sensor Networks for Home
Health Care, Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on, vol.2, no.,
pp.832-837, 21-23 May 2007.
• Kevin D. Blanchet. Telemedicine and e-Health. March 1, 2008, 14(2): 127-130. doi:10.1089/tmj.2008.9989.
• Lorincz, K., Kuris, B., Ayer, S. M., Patel, S., Bonato, P., and Welsh, M. 2007. Wearable wireless sensor network to assess clinical status
in patients with neurological disorders. In Proceedings of the 6th international Conference on information Processing in Sensor Networks
(Cambridge, Massachusetts, USA, April 25 - 27, 2007). IPSN '07. ACM, New York, NY, 563-564.
• Patel, Shyamal; Lorincz, Konrad; Hughes, Richard; Huggins, Nancy; Growdon, John H.; Welsh, Matt; Bonato, Paolo, Analysis of Feature
Space for Monitoring Persons with Parkinson's Disease With Application to a Wireless Wearable Sensor System, Engineering in
Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pp.6290-6293, 22-26 Aug. 2007.
Back to Sensors
283
189 SHIMMER Wireless Sensor Platform
189.1 SHIMMER Wireless Sensor Platform
SHIMMER is a small sensor platform designed for wearable applications by Intel's Digital Health Group. The platform features an integrated 3-axis
accelerometer, large storage (micro SD card), and low-power standards based communication capabilities on the based board. It supports standalone
application as such as motion capture. Additional sensing capabilities can be added via extension boards which connect to the base platform via Hirose
20 position connector. The platform is fundamentally a radio agnostic platform supporting both the 802.15.4 and Bluetooth standards in a low-power
system architecture.
284
189.2.1 Sensing
• 3-Axis Accelerometer using Freescale MMA7260Q 1.5/2/4/6g Micropower MEMs Accelerometer into CPU A/D
189.2.1.1 I/O
• Reset button
189.2.1.2 Expansion
• Hirose ST60 series 18 position rugged mobile style external Header for charging, programming, and tethered sensor extensions (12
multi-purpose I/O connections).
• Hirose DF12 series 20 position internal Expansion header for internal sensor daughter boards (14 Multi-purpose I/O connections)
189.2.1.3 Radios
• 802.15.4 Radio
♦ Chipcon CC2420
♦ GigaAnt Rufa 4.1dBi Antenna
• Class 2 BluetoothTM Radio
♦ WML-C46N CSR based design
285
189.2.1.4 CPU
189.2.1.5 Storage
• MicroSD slot (Up to 1 GB currently available)
189.3 Application
The goal of SHIMMER is to provide an extremely compact extensible platform for long-term wearable sensing in both connected and disconnected
settings using proven system building blocks. The design is realized using conventional module design and assembly technology to ensure repeatability
and economy.
189.3.1 Current applications
The TRIL Centre is currently using SHIMMER to capture kinematic and physiological data during gait analysis. Harvard Medical have demonstrated the
use of the use of the SHIMMER platform for home monitoring of people with Parkinson's Disease.
189.4 Power
• Design target is 10 days while sampling 6 channels at 50Hz w/250mAH battery.
• "Deep Sleep" shelf life is >1 year per battery spec
• Integrated Li-ion battery charger
• Ability to monitor and indicate power status
Software
• SHIMMER firmware is based on TinyOS. Sample code and applications can be found here: http://sourceforge.net/projects/shimmer/
• High level application support is available in the BioMOBIUS software environment.
Additional Information
• SHIMMER Hardware guide
• SHIMMER user pages
• SHIMMER is commercially available from Realtime Technologies
286
190 Sweden
190.1 Reimbursement Model in Sweden
For home telehealth in principle at least, an application could be made to the local county council for a telehealth installation (if such is available) and
these would be assessed on a case-by-case basis. There are no standard eligibility criteria set. In general, such services would be free-of-charge
although this might vary as each county council sets its own policy on this issue. This means that at some point in the future telehealth may be charged
for (e.g. through some level of co-payment in the same manner as for other health services), but this is currently not the case. Some telehealth services
are used throughout Sweden, like online patient journals and digital prescriptions. Also, some home telehealth pilot projects have been carried out, but
usually have not been incorporated into mainstream medical care for older people. However, Sweden is working to resolve this issue primarily via its
National IT Strategy.
287
191 TELOSB
191.1 TELOSB\T-Mote\Sky-Mote
Developed by the University of California, Berekely. It was a new mote design based on experiences with previous mote generations. Berekely designed
theTelos with three major goals in mind to enable experimentation: minimal power consumption, easy to use, and increased software and hardware
robustness. The use of the MSP430 in Telos gave it apower profile almost one-tenth the consumption of their previous mote platforms
http://www.hurray.isep.ipp.pt/activities/WSN/GetFile.aspx?File=crossbow.jpg
Selection of TelosB sensors [1]
Sensing:
I/O:
Integrated Humidity, Temperature, and Light sensors
16-pin expansion support and optional SMA antenna connector
802.15.4 Radio
Radios:
• Chipcon CC2420
• Integrated onboard antenna with 50m range indoors / 125m range outdoors
CPU:
Uses the ST M25P80 40MHz serial code flash for external data and code
Storage:
storage.
• Up to 1024kB of data can be stored
191.3 Application
191.4 Power
191.5 Software
191.7 Papers
288
192 TMote Sky
192.1 TELOSB/TMote Sky
Sensing:
I/O:
802.15.4 Radio
Radios:
• Chipcon CC2420
CPU:
Storage:
storage.
192.3 Application
Environmental Sensors
192.4 Power
Thesed motes may be powered by two AA batteries and were designed to fit the two AA battery form factor. AA batteries from 2.1 to 3.6V DC may be
used but for programming, the minimum power that must be supplied is 2.7V DC. Power will be drawn from the USB port (3V DC) whilst the device is
connected directly to a host computer which negates the need for batteries if this is the common use factor of the device. The 16-pin expansion
connector can also be used to provide power provided that power does not exceed the maximum value of 3.6V DC.
192.5 Software
Contiki, TinyOS, SOS and MantisOS Support
• TelosB Datasheet
• TMote Sky Datasheet
• TelosB by Crossbow
• Differences between TMote Sky and TelosB
• Moteiv Hardware Product Transition Notice
• TMote Support
192.7 Papers
• H. Wang and B. Sheng and Q. Li. "TelosB implementation of elliptic curve cryptography over primary field." Technical Report
WM-CS-2005-12, Dept. of Computer Science, College of William and Mary, Oct. 2005.
• G. Werner-Allen, K. Lorincz, M. Welsh, O. Marcillo, J. Johnson, M. Ruiz, J. Lees, "Deploying a Wireless Sensor Network on an Active
Volcano," IEEE Internet Computing, vol. 10, no. 2, pp. 18-25, March/April, 2006.
289
• Otto, C. and Milenkovic, A. and Sanders, C. and Jovanov, E., "System architecture of a wireless body area sensor network for
ubiquitous health monitoring", Journal of Mobile Multimedia, Vol. 1, No. 4, pp307-326, 2006
• Farshchi, S. and Pesterev, A. and Ho, W.L. and Judy, J.W., "Acquiring High-Rate Neural Spike Data with Hardware-Constrained
Embedded Sensors", Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, 2006
290
193 Sky Mote
193.1 TELOSB\T-Mote\Sky-Mote
Sensing:
I/O:
802.15.4 Radio
Radios:
• Chipcon CC2420
CPU:
Storage:
storage.
193.3 Application
193.4 Power
Thesed motes may be powered by two AA batteries and were designed to fit the two AA battery form factor. AA batteries from 2.1 to 3.6V DC may be
used but for programming, the minimum power that must be supplied is 2.7V DC. Power will be drawn from the USB port (3V DC) whilst the device is
connected directly to a host computer which negates the need for batteries if this is the common use factor of the device. The 16-pin expansion
connector can also be used to provide power provided that power does not exceed the maximum value of 3.6V DC.
193.5 Software
Contiki, TinyOS, SOS and MantisOS Support
• TelosB Datasheet
http://www.sentilla.com/moteiv-transition.html
193.7 Papers
291
194 Texas
194.1 Texas Reimbursement Model
• The Medicaid agency recognizes physician consultations (teleconsultations) when performed using interactive video teleconferencing.
• Payment is on a fee-for-service basis, which is the same as the reimbursement for covered services furnished in the conventional, face to face
manner. Reimbursement is made at both ends (hub and spoke site) for the telemedicine services. Other health care providers, such as
advanced nurse practitioners and certified nurse midwives are allowed to bill, as are Rural Health Clinics and Federally Qualified Health
Centers.
• The State uses consultative CPT codes with the modifier "TM" to identify telemedicine services
292
195 The Helical (Coil)
This is similar to a spiral that is not flattened. Start with a piece of wire that is 2 or 3 times longer than a whip and wind it into a coil. The number of turns
on the coil will depend on wire size, coil diameter, and turn spacing. The coil will need to be cut to resonate, and can be fine tuned by spreading or
compressing the length of the coil. If the coil is wound tightly enough, it may be shorter than onetenth of a wavelength. This antenna tunes sharply,
requiring care in tuning. The real part of the antenna impedance is less than 20 ohms, and depends on the size of the coil and its orientation to ground.
For 433.9 MHz, we wound 14 turns of 22 gauge wire around a 0.25 inch (6 mm) form. When tuned, it?s length was just under one inch. The proximity of
this coil to ground makes a big difference in performance. When the coil runs near and parallel to ground, maximum gain is only -18 dBd. When the
loose end of the coil was pulled away from ground, as shown in the alternate version drawing, gain increased to -5.5 dBd, and the null became deeper.
The big problem with this antenna is the mechanical construction and it's bulky size. It can be easily de-tuned by nearby objects, including a hand, so it
may not be good for hand-held use.
293
196 The Loop
The loop is entirely different from a whip, in that both ends of the antenna are terminated. In this case, the end that is opposite the transmitter (or
receiver) is grounded. A capacitor is used to tune the antenna to a real impedance, instead of a coil. An advantage of a loop is that it is not easily
detuned by hand effects, although the impedance may still vary. The loop can be made small, does not require a round-plane, and takes no more space
than a short whip. For these reasons, loops are very common in hand-held devices.
There are some disadvantages. Small loop antennas have a reputation for poor gain. A small loop will have a very narrow bandwidth. This makes tuning
extremely critical. Tuning is often done with a variable capacitor, which adds to the cost, both parts and labour. If the loop is large enough, it may be
practical to use a non-variable capacitor. This requires careful adjustment in engineering stages, to ensure that it is properly tuned with a standard value
capacitor.
Our example loop antenna covers a 12 by 35 mm area on the end of a board. It is tuned to 433.9 MHz with a variable capacitor. This antenna is very
omni-directional, but had a gain of only -18 dBd. A larger loop should have improved gain.
294
197 The Patch
The Patch antenna is a very low profile design, which consists of a round or rectangular patch of metal very close to a groundplane. The patch is usually
printed on a circuit board and can be made as part of the enclosure. Antenna coverage is in any direction above the groundplane, or a hemispherical
area. The patch antenna does require a substantial amount of area on a PCB, which makes it more practical above 800 MHz. It has a narrow bandwidth
so care must be taken to tune the size of the patch carefully. It is sensitive to the thickness and dielectric constant of the PCB and small variations will
mistune the patch completely. It is also sensitive to coatings, but not extremely sensitive to hand effects.
A practical example for 916 MHz can fit into an area only 30 by 40 mm. The patch size is 27 mm wide by 38 mm long for a board thickness of 0.060
inch. A thinner board or higher dielectric can require cutting the antenna a little shorter. About one-tenth of an inch of board space should be left around
any ungrounded edge of the patch. One edge of the patch should be grounded with multiple PCB vias. The antenna is fed with a line crossing through
the grounded edge to the 50 ohm point on the patch, or by a transmission line coming up through the bottom of the PCB. The 50 ohm point is about 13
mm away from ground on our example patch. The 50 ohm point for any design can be found by moving the vias toward or away from the grounded
edge. The farther the feed is away from the ground vias, the higher the impedance will be.
This type of patch is not a full-size, half-wavelength patch, so performance is not as good as a larger size patch. A full-size patch has no grounded edge,
so vias are not required. Our example rectangular patch has a gain of -8 dBd. Placing the board against a larger sheet of metal will improve the gain by
another 4 dB. If the antenna is made wider than one inch, up to about 3 inches wide, a few more dB can be gained. Polarization is perpendicular to the
grounded edge. Gain is good in almost any direction where the patch can be seen, but drops rapidly when looking at the edge of the board.
295
The trapezoidal version allows for less length so that it can fit into smaller spaces. Patterns and behavior are the same, but the gain is a little lower. We
measured about -12 dBd maximum, on a 40 by 90 mm board.
296
198 The Short PCB Stub
One big advantage for the short whip is that it can be a trace on a PCB, with a chip inductor used to tune out the capacitive reactance of the antenna. If
the trace runs parallel to ground, the real part of the antenna impedance will be approximately 10 ohms. In a hand-held unit, the impedance will be
raised substantially through hand effects. For a tenth wavelength strip on a board with hand effects included, the antenna has a capacitive reactance of
about 150 ohms. At 433.9 MHz, this would require a 56 nH inductor to cancel the capacitive reactance of the 2.7 inch (70 mm) long line.
The radiation pattern will be fairly omnidirectional, with a shallow null along one axis. The polarization is roughly parallel with the edge of the board.
Tuning is not extremely critical, small variations in inductor value or antenna length will not have a great effect on performance. Our sample designs, at
433.9 and 916 MHz, resulted in maximum gains of between -12.5 to -14 dBd off the side of the board. The null dipped down to about -26 dBd. This is
more omnidirectional than some other designs, and hand effects will help to reduce the null depth.
The key to this design is to keep resistive losses low, use wide traces (if a PCB trace), and good quality inductors. Adjust the inductor value for
maximum output in the environment that it will be used. Gain can be improved by making the whip longer and thus reducing inductance. But, in some
cases, it may be better to shorten the trace and add inductance rather than to run the antenna close to other circuit board traces.
297
199 The Short Whip
A simple alternative to the whip is to make it shorter than a quarter wavelength and add an inductor near the base of the whip to compensate for the
resulting capacitive reactance. The inductor can be made by coiling up part of the whip itself. This type of antenna can have performance nearly equal to
that of a full size whip.
298
200 The Slot
Common in radar systems and/or on aircraft, a variation of the slot antenna may have potential above 800 MHz. A quarter-wave slot is cut into a metal
sheet or unetched PCB, and if enough area is available, will provide omnidirectional coverage. Our sample antenna at 916 MHz required a 75 mm long
PCB. The length of the slot was 59.5 mm for 0.060 inch (1.5 mm) thick FR4. A different thickness or dielectric will require changing the length of the slot.
One end of the slot must be left open. The slot was fed near the closed end, in this case 4 mm from the end. The feedpoint impedance can be adjusted
by moving the feed toward or away from the closed end. Tuning is somewhat critical.
When the board is horizontal, the pattern is omnidirectional around the edge of the board, thus horizontally polarized. We also see omnidirectional
coverage when the board is vertical (with the slot horizontal). In this case, polarization is vertical! It may not make sense, but a horizontal slot is
equivalent to a vertical whip in this case. Gain is -4.5 to -6 dBd. The feed can be a trace on the backside of the board, with a via used to make
connection with the top of the board near the slot.
299
201 The Spiral
Another way to shorten a whip is to coil it up to form a flattened coil of wire. It can be a trace printed onto a circuit board. On a board, the length of the
trace is a little shorter than a quarter wavelength. The antenna must not have a groundplane directly under it, and should occupy a clear end of the
board. For example, start with a six inch long thin trace wrapped in a 0.75 inch (19 mm) square area, then trim a little of the length until it resonates at
433.9 MHz.
Antenna gain and impedance will vary with the size of the groundplane. Our 433.9 MHz version had a fairly small groundplane area of 17 sq. cm, while
the 916 MHz version had a quarter-wave long ground. The 433.9 MHz antenna had a maximum gain of -10.5 dBd, with a small null of -24 dBd. The 916
MHz antenna had a gain of -5 dBd max. Comparable gain is also seen when looking at the board face-on.
This antenna does not give circular polarization; the polarization is parallel to the long edge of the board. As with a stub, when the board is oriented
vertically, it is vertically polarized and omnidirectional. This antenna is more easily detuned by a hand, which makes it less suitable for hand-held
remotes.
300
202 The structure of EMRs and EHRs and the boundaries of each
EMRs and EHRs (himmsanalytics)
Back to Digital Health Records
301
203 The Timed Up and Go Test
The Timed Up and Go Test is an indicator of basic mobility and measures the time required for a person to rise from a chair, walk three metres, turn,
walk back and sit down. Various trials have been conducted using the test with encouraging results. However questions remain over the accuracy of the
test. Use of the test may also be limited in very frail elderly subjects due to floor effects.
302
204 The Underpants that Could Save your Life
Besides the regular equipments like a cuff, a pump, and stethoscope or electronics available for measuring blood pressure which are bulky, Philips
consumer electronics company is keen on developing an underwear that actually monitors one's blood pressure continually. Each sensor recurrently
measures the electrical impedance of the tissue beneath it ? a property that changes as the pulse wave passes by. A pair of such sensors can calculate
the speed of the pulse wave by timing how long it takes to travel from one sensor to the other. Once calibrated with a conventional blood-pressure
reading, the electrodes can then give accurate blood-pressure readings, while the wearer enjoys the comfort of their own underpants.
Philips Prototype Blood Pressure Sensor
303
205 The Veterans Administration
The Veterans Administration (VA) appears to have taken the lead in providing home telehealth service for their clients (army veterans). Examples of
up-and-running services include those under the Care Coordination Home Telehealth (CCHT) programme (1). This is probably the most developed
example of mainstreaming of home telehealth in the US. It combines care coordination and the use of technology to serve a variety of veteran
populations that are high risk and high resource use, and thus represent high cost to the services. To facilitate early home placement, interactive
systems being deployed include videophones, telemonitoring devices, in-home messaging devices, a PC Web-based interactive system, specialized
instamatic cameras for wound care follow-up, and the telephone. Currently there are over 3,764 patients enrolled in the programs and over 7,000
patients have been treated in total.
304
206 References
• 1. http://www.baypines.va.gov/services/ccht.asp
305
207 TI CC2420
207.1 TI CC2420
The CC2420 is a single-chip true IEEE 802.15.4 compliant transceiver which works in the 2.4Ghz range. It includes a digital direct sequence spread
spectrum (DSSS) baseband modem encompassing an effective data rate of 250 Kbps and a gain of 9 dB. It is a low-cost solution intended use in the
unlicensed ISM band. It is designed for low power applications and is based on Chipcon's SmartRF®-03 technologhy in 0.18?m CMOS. It complies with
worldwide regulations covered by ETSI EN 300 328 and EN 300 440 class 2 (Europe), FCC CFR47 Part 15 (US) and ARIB STD-T66 (Japan).
The CC2420 provides extensive hardware support for packet handling, data buffering, burst transmissions, data encryption, data authentication, clear
channel assessment, link quality indication and packet timing information. These features reduce the load on the host controller and allow CC2420 to
interface low-cost microcontrollers.
The configuration interface and transmit/receive FIFOs of CC2420 are accessed via an SPI interface. In a typical application CC2420 will be used
together with a microcontroller and a few external passive components.
207.2 Applications
Typical applications for the CC2420 as proposed by the datasheet are:
• 2.4Ghz IEEE 802.15.4 Systems
• Wireless Sensor Networks
• ZigBee Systems
• Home/building automation
• Industrial Control
• PC peripherals
• Consumer Electronics
207.3 Features
• True single-chip 2.4 GHz IEEE 802.15.4 compliant RF
transceiver with baseband modem and MAC support
• DSSS baseband modem with 2 MChips/s and 250 kbps effective
data rate.
• Suitable for both RFD and FFD operation
• Low current consumption (RX: 18.8 mA, TX: 17.4 mA)
• Low supply voltage (2.1 ? 3.6 V) with integrated voltage
regulator
• Low supply voltage (1.6 ? 2.0 V) with external voltage regulator
• Programmable output power
• No external RF switch / filter needed
• I/Q low-IF receiver
• I/Q direct upconversion transmitter
• Very few external components
• 128(RX) + 128(TX) byte data buffering
• Digital RSSI / LQI support
• Hardware MAC encryption (AES-128)
• Battery monitor
• QLP-48 package, 7x7 mm
• Complies with ETSI EN 300 328, EN 300 440 class 2, FCC
CFR-47 part 15 and ARIB STD-T66
• Powerful and flexible development tools available
207.4 Interfacing
The CC2420 comes in a QLP48 package as described by the datasheet. This package is equivalent to the JEDEC Standard QFN (Quad Flat No-Lead)
package.
(see datasheet for specific dimensions)
207.5 Configuration
The CC2420 is programmed via 4-wire serial bus thus making CC2420 a flexible and easily programmed transceiver.
207.6 Currently Used In
• Atlas
306
• BSN Node
• FireFly
• IMOTE2
• MICAz
• Particles (zPart)
• Porcupine
• SHIMMER
• TELOSB/TMote Sky
207.7 References and Additional Information
• CC2420 Datasheet
• CC2420 Documentation - TinyOs living document of the CC2420)
307
208 T-Node
208.1 T-Node (SOWNet Technologies platform)
The T-Nodes are the original SOWNet Technologies platform, developed by TNO Defence, Security and Safety for wireless sensor networks
research.They combine a microcontroller for local processing, a radio for digital communications and I/O abilities to interface with sensors, actuators and
external systems.
http://www.sownet.nl/images/stories/T-Node%20in%20hand.jpg
A number of interfaced sensors are available non-limiting to:
Sensing:
• Alcohol
• Temperature
• Passive Infrared
• Relative Humidity
• Heartbeat
• Light
• Magnetic
Two 10 pin expansion connectors with:
I/O:
• Analog inputs
• 10-bit ADC
• Interrupts
• Digital I/O
• I²C
• SPI
• UART interfaces
868 MHz FSK Transceiver with frequency hopping
Radios:
• 120m Range in free space outdoors and 40m indoors
• Self-organizing ultra low power multi-hop protocols
Amtel AtMega128L
CPU:
• 128 k bytes programmable flash memory
• 4 kbytes SRAM
• 512 k bytes data flash memory (Optional/on request)
• Dual UART
• Other interfaces
♦ I2C, SPI, Digital I/O
♦ AD converter
♦ 8 channel 10 bit
Storage:
208.3 Application
T-Nodes can be used for and implemented in:
• Building management systems
• Micro-climate monitoring like Relative Humidity moni-toring
• Dynamic evacuation systems (DES)
• Seat monitoring in the public railway sector
• Fire systems
• Intruder systems
• Men guarding
• Access Control systems
• Temperature monitoring (HVAC systems)
208.4 Power
The main power drain is the radio, so nodes conserve power by turning off their radio and going into sleep mode whenever possible. However, to
receive data, their radio must be on. Special low power communication protocols can reduce power consumption by ensuring the radio is only on when
needed. In addition to this power saving on the radio level, much can be gained by using an appropriate type of network.
308
208.5 Software
TinyOS
• SOWNet Technologies
• T-Node product sheet
• See also L-Node
208.7 Papers
309
209 TRIL Falls Gait Analysis Platform
The TRIL Centre have developed a gait analysis system with the following major components:
Floormat sensor
Body Worn Sensors (Physiological/Kinematic parameters)
Motion video capture
Software/UI for clinicians
The body worn sensor capabilities for the Gait analysis were provide by the SHIMMER wireless sensor platform. The SHIMMER units are controlled by
and communicate data to a master PC via a Bluetooth radio stack. The application software developed using the BioMOBIUS environment supportsdata
capture at rates up to 100Hz for kinematic SHIMMERs and up to 500Hz for ECG SHIMMERs. During clinical trials three SHIMMERs are worn by the
subject. Two kinematic SHIMMERs, one attached to each leg are used to determine the temporal parameters of gait. A third SHIMMER unit attached to
the chest using three electrodes measures the [ECG] signal of the heart from which heart rate may be determined.
The floor-mat technology used in the gait analysis platform is based on Tactex Control Inc. floor sensors. These sensors provide bespoke pressure
sensitive walkways, designed to analyze gait. The walkway consists of 45 pressure-sensitive tiles, developed specifically for TRIL. The floor sensor
measures pressure under the foot as the person walks along the walkway. The sensor can therefore be used to detect the location and timing of each
footfall, as well as pressure changes under the foot during gait. BioMOBIUS blocks were developed to acquire, store and The system's user interface
allows clinicians to select and adjust what data that is collected and how the data is processed. The software encapsulates data acquisition and signal
processing modules, and allows customization of the sensors.
310
210 TRIL Gait Analysis Platform
The TRIL Centre have developed a gait analysis system with the following major components
• Floormat sensor
• Body Worn Sensors (Physiological/Kinematic parameters)
• Motion video capture
• Software/UI for clinicians
The body worn sensor capabilities for the Gait analysis were provide by the SHIMMER wireless sensor platform. The SHIMMER units are controlled by
and communicate data to a master PC via a Bluetooth radio stack. The application software developed using the BioMOBIUS environment supportsdata
capture at rates up to 100Hz for kinematic SHIMMERs and up to 500Hz for ECG SHIMMERs. During clinical trials three SHIMMERs are worn by the
subject. Two kinematic SHIMMERs, one attached to each leg are used to determine the temporal parameters of gait. A third SHIMMER unit attached to
the chest using three electrodes measures the ECG signal of the heart from which heart rate may be determined. The floor-mat technology used in the
gait analysis platform is based on Tactex Control Inc. floor sensors. These sensors provide bespoke pressure sensitive walkways, designed to analyze
gait. The walkway consists of 45 pressure-sensitive tiles, developed specifically for TRIL. The floor sensor measures pressure under the foot as the
person walks along the walkway. The sensor can therefore be used to detect the location and timing of each footfall, as well as pressure changes under
the foot during gait. BioMOBIUS blocks were developed to acquire, store and The system's user interface allows clinicians to select and adjust what
data that is collected and how the data is processed. The software encapsulates data acquisition and signal processing modules, and allows
customization of the sensors.
TRIL Gait Analysis Platfrom User Interface. The clinician has control over what sensors are being used and which parameters to be acquired
The software simultaneously acquires real-time data from SHIMMER-based sensors, the floor sensor and 2 webcams
311
211 Tunstall Fall Sensor
http://www.tunstall.co.uk/assets/Literature/6_1_41falls_management_solutions_sheet.pdf
312
212 UK
212.1 Reimbursement Model in UK
Home telehealth for older people and others with chronic conditions is beginning to take off in the UK, even if such services are currently only available
in some localities and are typically tied to specific hospital services (trusts) and medical conditions. As an example, Northern Ireland?s Department of
Health and Social Services is getting set to issue a tender for the supply of telehealth services to cover 5,000 people by 2011. Northern Ireland will
invest £46m in telemedicine services to support chronic disease management. For home telehealth services, to the extent that they have been
mainstreamed in principle at least, services are free to the end-user under the National Health Service.
313
213 UK Government Intervention
The UK Government have identified (1) four key stages to justifying the rational for government intervention. These are as follows;
• Identify the set of policy goals to be achieved: - This involves an assessment of the Government's strategic goals and objectives, and the way
in which they are translated to individual policy areas.
• Identify why these goals may not be delivered without government activity.
♦ Imperfect competition (market power). Economic theory demonstrates efficient outcomes will be delivered only where markets are
actually or potentially competitive. This section deals with monopolies and monopsonies and includes predatory pricing.
♦ Externalities. An example of a positive externality is the spill over effect into other areas that can occur as a result of research and
development activity. A company or research institution will generally decide its level of R&D on the basis of the benefits that it can
capture - ignoring benefits that might occur elsewhere. In the instance of telemonitoring it is very likely that for example a targeted
program around weight management and obesity will bring spill over benefits to Heart Failure, diabetes and pulmonary disorders.
An example of a negative externality is pollution of the environment.
♦ Information failures. Information failures lead to sub-optimal outcomes. For example, a buyer may not have full information on the
characteristics of a product or service he/she wishes to buy..
♦ Public goods. True public goods and services are comparatively rare, but the provision of national defence and of law and order are
typically used as illustrations. In the telemonitoring scenario, provision of privacy and information security and legal protection are
examples.
• Equity, which is to do with the delivery of social or distributional objectives. Even where markets are working efficiently, they may result in a
distribution of income (or other benefits/costs) that is unacceptable to society. This will often arise through a lack of incentives to improve
equity, or because the necessary information is available only to government. A classic example here is the cost of telehealth solutions. If the
cost issue is not addressed then an inequality will exist where more affluent persons will be able to have access to better healthcare services,
thus widening further the inequality gap. This is why reimbursement policy is of such importance.
• Identify what actions are available to government in order to deliver the desired outcomes. A wide range of interventions is available to
government, and it will often be appropriate to consider several options. Examples fro telehealth adoption include tax incentives,
reimbursements, grants, loans, and information campaigns.
• Consider whether the costs of government intervention are justified. This is concerned with the cost analysis of the intervention both positive
and negative and the cost of not intervening. It is a full cost breakdown cosnidering the various options. This is an absolute MUST for
telehealth adoption .ie. government must lead a full business case analysis of the cost of doing it against the cost of not doing it.
314
214 References
• 1. http://interactive.cabinetoffice.gov.uk/strategy/survivalguide/skills/ao_rationale.htm
315
215 Useability
This section will look at how body sensor networks can be used in peoples everyday lives and operate as unobtrusively as possible. The ideal scenario
is that person being monitored does nothing at all extra but goes about his/her daily routine as normal, all the while the sensor network is collecting
valuable data. We have a ways to go before these sensors will become truly pervasive in this sense, however we will look at some examples of systems
that are advancing this cause and give some details of the design aspects involved.
• Eco: Ultra-Wearable and Expandable Wireless Sensor Platform [1]- Very tiny and flexible sensor developed at University of California capable
of being used to monitor physiological data. Being used to assess sports and art perfromances.
• WEALTHY Project - Wearble Healthcare Systems for vital signs monitoiring [2]- This is a European Union funded project (IST-2001-37778)
which started in September 2002 and ran for two and a half years. It is one of the pioneering EU projects laying the groundwork research in to
fabrication of garments suitable for monitoring of vital signs. It demonstrates how electrics/electronics and textiles can be merged and also
demonstrates how robust and useable these materials can be.
• MyHeart Project [3] - The MyHeart project is an Integrated Project, funded by the IST programme of the European Commission's 6th
Framework. It started on December 31st, 2003 and ran for 45 months. The MyHeart consortium consists of 33 different partners from 11
countries and was led by Philips. MyHeart aims at fighting Cardio Vascular Disease by preventive lifestyle and early diagnosis. A key element
of this work is the continuous measurement of vital signs using sesnor systems embedded into functional cloths.
• Project STELLA - Stretchable Electronics for Large Area Applications [4] - The STELLA project is an EU funded project (IST-028026) with
eleven partners in 4 European countries. The project started in January 2006 and wil run until January 2010. It is part of a greater initiative
called SHIFT ?Smart High Integration Flex Technologies [5]. The STELLA project is concerned with stretchable electronics i.e. printed circuit
boards and circuits that can literally be stretched and deformed and still maintain functionality. The STELLA approach is more about traditional
electronics being innovated rather that textiles or other wearable materials research.
• CONTEXT Project - Contactless Sensors for Body Monitoring Incorporated in Textiles[6] - The ConText project is an Specific Targeted
REsearch Project (STREP), funded by the IST program of the European Commission's 6th Framework. ConText is started on January 1st,
2006, and ran for 30 months. Contact less sensors were developed for the purpose of measuring Electromyography (EMG) and
Electrocardiography (ECG) signals. The sensors were integrated into textiles to realise a prototype of a wearable vest.
• OFSETH - Optical Fibre Sesnors Embedded in to technical Textiles for Healthcare monitoring[7] - The OFSETH project is a EU Specific
Targeted Research Project (STREP) FP6-IST-2005-027869 that started in March 2006 and runs until end 2009. The aim of OFSETH is to
take advantage of pure optical sensing technologies for extending the capabilities of medical technical textiles for wearable health monitoring
316
216 User-activated alarms and pendants
The most popular fall detection device are user-activated alarms and pendants. These devices, generally, require the user to manually activate an alarm
button. Usually the alarm button is on a pendent or a bracelet integrated with a wireless transmitter or transceiver, to be pressed in the event of falling.
When the alarm button is pressed it wirelessly activates some form of alert unit that that has external connectivity normally via a pots line or alternatively
some form of broadband connection. The alert system notifies a remote monitoring center which responses to the alarm. A number of elderly care
facilities and community alarm centers offer such services for a nominal monthly monitoring fee.
Alternatively there has been interest in the building the sensor in garments worn the patient, such as the EU funded project HEBE. Issues
User activated alarms and pendants offers a low-cost and technological simple approach to the problem, but they are not suitable for falls associated
with the loss of consciousness or for subjects who due to physical impairment cannot activate the alarm. They are likely to be suitable for people who
are suffering from some form of cognitive impairment e.g. dementia [1]
216.1 Justification
(Scientific Basis/efficacy/evidence)
216.2 Research
• Projects
♦ EU funded project: HEBE. Detection of falls and monitoring of the elderly system is fitted with a GPS locating device, a bi-axial
accelerometer (for the detection of falls and of activity), all connected to a call centre using GSM/GPRS technology.
216.3 Funding
• National Health Services
• Insurance Companies
• Individuals
• Family Members
216.4 Commercial
There are a commerical of user activated alarms and pendants commerically available.
216.4.1 Products
•
♦ Philips Life Line
◊ Classic Pendant Personal Help Button
◊ Slimline Personal Help Button
◊ Tempo Watch
♦ BLEEP
♦ Lifefone
♦ MedicalAlarm.com
http://medicalalarm.com/Medical_Alarm_and_Medical_Alert/IMAG012A.GIF
216.4.2 Players
•
♦
◊ Philips Life Line
◊ BLEEP
◊ Lifefone
◊ MedicalAlarm.com
216.5 Standards
216.6 Gaps
• Gaps in technology
• Gaps in the basic science
• Gaps in operation
317
• Gaps in implementation
216.7 Future Vision
216.8 References
1. P. Rajendran, A. Corcoran, B. Kinosian, and M. Alwan, Falls, Fall Prevention, and Fall Detection in Technologies Aging Medicine in Eldercare
Technology for Clinical Practitioners, Humana Press; 1 edition (December 14, 2007)
2. M.E. Tinetti and M. Speechley, Prevention of falls among the elderly, NEJM (April 20 1989)
3. F. Miskelly, Assistive technology in elderly care, Age ageing 30:455-458 (2001)
318
217 Vibering
The hearing impaired, now have a new agent to aid them with their impairment, if not eliminate it completely. Vibering, created by designers
Kwang-Seok Jeong, Min Hee Kim and Hyun Jung Kim, is a device that fashionably houses a sound detection and identification system to be worn as a
pair of rings and a wristwatch. When the rings are worn on both hands and the ears, they determine distance, position and vibrate according to the
source. The watch is automated to listen and interpret key phrases like ?Excuse Me?, your name being called and car horns.
Vibering for the hearing impaired
319
218 Video monitoring-based fall detectors
218.1 Introduction
There has been significant interest in the use of video cameras in the home to detect falls in older people. From a technical perspective video camera
promise an effective non contact method of falls detection. However there is strong resistance from older people against the deployment of cameras in
homes due to privacy concerns. Methods of hiding identities have been developed such as face or whole body bluring. To date no success methods to
circumvent the privacy issues have been developed.
218.2 Research Projects
Video monitoring systems use camera?s which attempt to detect a fall event based on image-processing algorithms that are designed to identify
unusual inactivity, which is more likely to follow an event of fall. The fall detectors under this category are passive in the sense that they generally do not
require the user to wear any device. This form of approach remains an active area of research within the academic community focusing design
variations in image-processing algorithms and monitoring/transmission systems [1].
The UbiSense project [2], at the Imperial College London, is focused on developing an unobtrusive health-monitoring system for the elderly by using
embedded smart vision techniques to detect changes in posture, gait, and activities. In addition the UbiSense system attempts to capture signs of
deterioration of the patients by analyzing subtle changes in posture and gait. Privacy issues are address in UbiSense by immediately filtered the images
at the device level into blobs, which encapsulate only the shape outline and motion vectors of the subject. Visual images are not stored or transmitted
therefore it is not possible to reconstruct the abstracted image back into the original image.
The University of Dundee, has investigated the use of ceiling mounted camera?s which has the advantage of avoiding the problems associated with
furniture occlusion [3]. Patterns of inactivity are used to make inferences about health and also to help detect falls. The University of Liverpool have
reported the Smart Inactivity Monitor Using Array-Based Detectors (SIMBAD) fall detector based on a low-cost array of infrared detectors to capture
low-level image of the resident and then analyzes the subject?s motion to detect a fall event [4]. The IRISYS sensors are designed to provide
non-intrusive monitoring due to the low resolution of the images. Falls are detected using a neural model based on the velocity and acceleration of the
tracked object. The findings indicated good specificity in terms of low false-alarm rates; however the model could only detect 30% of the emulated falls.
Despite the ability of video-based fall-monitoring systems to automatically detect falls with no user intervention, the fear of intrusion of privacy are
extremely prominent in this technology approach. Although a variety of solutions have been developed to ensure privacy people in homes still
experience the feeling of ?being-watched? thus making the technology unacceptable in many cases.
218.3 References
1. P. Rajendran, A. Corcoran, B. Kinosian and M. Alwan, Falls, Fall Prevention, and Fall Detection Technologies in Eldercare Technology for
Clinical Practitioners, Humana Press, pp 187-202, 2008.
2. http://www.doc.ic.ac.uk/vip/ubisense/home/home.html
3. C. H. Nait and S. J. McKenna, Activity summarisation and fall detection in a supportive home environment, International Conference on
Pattern Recognition (ICPR), August 23-26, Cambridge, UK, 2004.
4. A. Sixsmith and N. Johnson, A Smart Sensor to Detect the Falls of the Elderly, Pervasive Computing, Vol. 3, No. 2, April-June, 2004, pp
42-47.
320
219 Viterion TeleHealth Network
Viterion TeleHealthCare Network
Viterion - (a Bayer Healthcare Company) offers a range of products for monitoring blood pressure, pulse oxygen, weight, peak flow (spirometer),
asthma, blood glucose and temperature. They also have a manual entry system for treatments such as pain management. Viterion offer a ?full service?
from devices (Panasonic) to the monitoring, to full clinical intervention via their ?Telehealth Network? (1). Viterion manage the network and ensures its
security, reliability and manages all access (for doctor/nurse/caregiver etc).
321
220 References
• 1. http://www.viterion.com/web_docs/V200%20Ad.pdf
322
221 Vivometrics Lifeshirt
LifeShirt® is a form-fitting vest that provides clinics with high-quality data collection and a non-invasive alternative to confidently test their patients for
sleep apnea ? at the clinic or their home. LifeShirt® delivers sleep lab science in a comfortable, easy to wear garment.
• Provides analysis of breathing patterns as an aid in classifying apneas
• Produces nearly identical results to in-lab PSG for the diagnosis of OSA(1)
• Incorporates respiratory inductance plethysmography (RIP) technology
•
The LifeShirt® System has received the following market clearances and certifications: US FDA 510 (k) market clearance, Health Canada market
clearance, European CE Mark certification, ISO 13485 registration.Distinguishes Obstructive Sleep Apnea from Central Sleep Apnea(2)
Vivometrics Lifeshirt
323
222 WEALTHY Project - WEARABLE HEALTH CARE SYSTEM FOR VITAL SIGNS
MONITORING
Strain fabric sensors based on piezoresistive yarns, and fabric electrodes realized with metal based yarns, enable the realization of wearable and
wireless instrumented garments capable of recording physiological signals, to be used during the routinely activity, to be wear instead of a classical
garment without any discomfort for the user.Piezoresistive fabric sensors have been realised by using lycra fabric coated with carbon loaded rubber
and commercial electroconductive yarn ( PAC 250 dtx x 1 , by Europa NCT, Poland). These fabrics behave as strain gauge sensors and show
piezoresistive properties in response to an external mechanical stimulus. The coated lycra® fabric has been used to detect respiration signal, due to the
higher efficient shown in term of quality of the signal, compared with the other fabric sensor. The Europa yarn has been used for the activity sensors and
knitted in the multifunctional fabric.
To improve the electrical signal quality in dynamic condition a hydro gel membrane purchased by ST&D Ltd (Belfast-UK), has been used. This affects
also the comfort as electrodes have a rough surface and a prolonged contact with the body can give rise to skin irritations
Washability and reusability Conductive yarns and fabrics are resistant to repeated washing in aqueous solutions, the washed electrodes can be used
to detect ECG signal, the signals detected shown that the performances of the conductive fabric are not affected by the washing process.
324
223 WEALTHY Project - Wearable Healthcare Systems for vital signs monitoring
The main objective of WEALTHY [1] is to set up a comfortable health monitoring system. This will be based on a "wearable" interface, implemented by
integrating smart sensors (in fiber and yarn form), advanced signal processing techniques and modern telecommunication systems on a textile platform
and by developing a monitoring system for data management with local intelligence in the form of a decision support unit.
Key focus areas are the assistance of cardiac patients during rehabilitation and also assist professional workers subject to considerable physical and
psychological stress and/or environmental and professional health risks.
Conducting and piezoresistive materials in form of fiber and yarn are integrated in a garment and used as sensor and electrode elements. The
simultaneous recording of vital signs allows parameters? extrapolation and inter-signalelaboration that contribute to produce alert messages and
synoptic patient table.
garment without any discomfort for the user.
WEALTHY Project Set Up
325
Sensor Outputs
Piezoresistive fabric sensors have been realised by using lycra [2]® fabric coated with carbon loaded rubber and commercial electroconductive yarn (
PAC 250 dtx x 1 , by Europa NCT, Poland [3]). These fabrics behave as strain gauge sensors [4] and show piezoresistive properties [5] in response to
an external mechanical stimulus. The coated lycra® fabric has been used to detect respiration signals, due to the higher efficiency shown of signal
quality, compared with the other fabric sensor. The Europa yarn has been used for the activity sensors and knitted in the multifunctional fabric.
To improve the electrical signal quality in dynamic condition a hydro gel membrane purchased by ST&D Ltd (Belfast-UK)[6], has been used. This affects
Washability and reusability - Conductive yarns and fabrics are resistant to repeated washing in aqueous solutions, the washed electrodes can be
used to detect ECG signals and the signals detected shown that the performances of the conductive fabric are not affected by the washing process.
326
224 WEALTHY Project - Wearble Healthcare Systems for vital signs monitoiring
1. REDIRECT WEALTHY Project - Wearble Healthcare Systems for Vital Signs Monitoring
327
225 WEALTHY Project - Wearble Healthcare Systems for Vital Signs Monitoring
The main objective of WEALTHY [1] is to set up a comfortable health monitoring system. This will be based on a "wearable" interface, implemented by
integrating smart sensors (in fiber and yarn form), advanced signal processing techniques and modern telecommunication systems on a textile platform
and by developing a monitoring system for data management with local intelligence in the form of a decision support unit.
Key focus areas are the assistance of cardiac patients during rehabilitation and also assist professional workers subject to considerable physical and
psychological stress and/or environmental and professional health risks.
Conducting and piezoresistive materials in form of fiber and yarn are integrated in a garment and used as sensor and electrode elements. The
simultaneous recording of vital signs allows parameters? extrapolation and inter-signalelaboration that contribute to produce alert messages and
synoptic patient table.
garment without any discomfort for the user.
WEALTHY Project Set Up
328
Sensor Outputs
Piezoresistive fabric sensors have been realised by using lycra [2]® fabric coated with carbon loaded rubber and commercial electroconductive yarn (
PAC 250 dtx x 1 , by Europa NCT, Poland [3]). These fabrics behave as strain gauge sensors [4] and show piezoresistive properties [5] in response to
an external mechanical stimulus. The coated lycra® fabric has been used to detect respiration signals, due to the higher efficiency shown of signal
quality, compared with the other fabric sensor. The Europa yarn has been used for the activity sensors and knitted in the multifunctional fabric.
To improve the electrical signal quality in dynamic condition a hydro gel membrane purchased by ST&D Ltd (Belfast-UK)[6], has been used. This affects
Washability and reusability - Conductive yarns and fabrics are resistant to repeated washing in aqueous solutions, the washed electrodes can be
used to detect ECG signals and the signals detected shown that the performances of the conductive fabric are not affected by the washing process.
329
226 WeBee
226.1 WeBee
WeBee3 is a miniaturised, battery operated sensor for less than ?10. The heart of the IEEE 802.15.4/ZigBee hardware node is a CC 2431 type SoCfrom
TI/Chipcon. The onboard ZigBee stack allows to fully integrate WeBee3 as an end device into ad-hoc mesh networks. As a temperature sensor WeBee3
can work for over 10 years without replacing or recharging the battery.
http://www.snm.ethz.ch/pub/uploads/Projects/WeBee3.jpg
Sensing:
I/O:
Radios:
Analog and Digital IOs
• 2.4 GHz IEEE 802.15.4 Transceiver
• TI-Chipcon CC2430 / CC2431
• Fractus Slim Reach Xtend antenna
8051 Microcontroller
CPU:
• 128 KB Flash
• 8 KB RAM
Storage:
226.3 Application
• Wireless Sensor Networks
• WPANs
• Home Automation
• Building Automation
• Industrial Automation
226.4 Power
The WeBee3 is run on a Sanyo CR1/3NT1 3V; 160 mAh button cell. This cell may provide several years of functionality to the WeBee3 depending on
the application.
226.5 Software
• WeBee flyer
• CEESAR Swiss Research Center for Smart Living
• i|Home|Lab
• Sensor Network Museumtm
226.7 Papers
• Klapproth, A.; Bissig, S.; Venetz, M.; Knauth, S.; Käslin, D.; Kistler, R.; "Design of a versatile lowcost IEEE802.15.4 module for long term
battery operation," 1 s t European ZigBee Developers Conference, EuZDC 2007, June 18th-20th, Germany
Back to Sensors
330
227 WeeBee
227.1 WeBee
WeBee3 is a miniaturised, battery operated sensor for less than ?10. The heart of the IEEE 802.15.4/ZigBee hardware node is a CC 2431 type SoCfrom
TI/Chipcon. The onboard ZigBee stack allows to fully integrate WeBee3 as an end device into ad-hoc mesh networks. As a temperature sensor WeBee3
can work for over 10 years without replacing or recharging the battery.
Sensing:
I/O:
Radios:
Analog and Digital IOs
• 2.4 GHz IEEE 802.15.4 Transceiver
• TI-Chipcon CC2430 / CC2431
• Fractus Slim Reach Xtend antenna
8051 Microcontroller
CPU:
• 128 KB Flash
• 8 KB RAM
Storage:
More information will be available soon
227.3 Application
227.4 Power
227.5 Software
227.7 Papers
331
228 Wilomena and Will's Story
This CAPSIL is under development and will come online April 15
Will/Waseda responsible
332
229 Wireless body area network of intelligent motion sensors for computer
assisted physical rehabilitation
Jovanov et al in this proposed a general system architecture and describe a recently developed activity sensor called "ActiS". ActiS is based on a
standard wireless sensor platform and a custom sensor board with a one-channel bio amplifier and two accelerometers. As a heart sensor, ActiS can be
used to monitor heart activity and position of the upper trunk. The same sensor can be used to monitor position and activity of upper and lower
extremities. A wearable system with ActiS sensors would also allow one to assess metabolic rate and cumulative energy expenditure as a valuable
parameter in the management of many medical conditions. An early version of the ActiS has been based on a custom developed wireless intelligent
sensor and custom wireless protocols in the license-free 900 MHz Scientific and Medical Instruments (ISM) band.
The ActiS sensor was developed specifically for WBAN-based, wearable computer-assisted, rehabilitation applications. ActiS consists of a standard
sensor mote platform, Telos, from Moteiv and a custom Intelligent Signal Processing Module ? ISPM
An example application of the ActiS sensor as motion sensor on an ankle used to measure angular position and velocity for rehabilitation purposes.
Jovanov et al Ankle rehab sensor based on ActiS platform
Jovanov et al Step recognition output from sensor module
333
230 Wireless Emissions and EMC
Another issue to be considered here is the body effects on RF signals i.e. how the body effects RF signals. Higgins has shown that antenna design is
crucial isn body sensor networks and teh increas in dielectric constant from having a human body around actually works in the designers favour in that
smaller antenna can be used.(physically small antennas though do have their drawbacks also!)
334

Annex II: CAPSIL Wiki Copy

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