On the Clinical Evaluation of Smart Driving Assistance for

On the Clinical Evaluation of
Smart Driving Assistance for Power Wheelchairs
Christian Mandel, Thomas Röfer, and Insa Lohmüller
Abstract— The success of assistive robotic systems in the
field of rehabilitation and compensation of neurological disabilities strongly depends on their balance between deploying
supporting technologies and maintaining human independence.
This work examines the impact of a navigation solution for an
assistive robotic wheelchair, covering the requirements given
above from both sides. The system, i.e. a smart driving assistant,
supports the user in controlling his/her wheelchair by correcting
insecure commands. It is evaluated within a clinical study,
recruiting ten subjects in two groups (i.e. group A with driving
assistance, and group B without). Participants from both groups
apply a common hand-joystick and a newly-developed headjoystick within a standardized wheelchair parcours. Findings
indicate a slight decrease in average speed and smoothness of
the driven trajectories when performing with driving assistance,
but a huge decrease in terms of average number of collisions.
Finally we show that under a decreased level of cognitive
challenges when performing with driving assistance, the average
number of falsely executed, neglected, or aborted obstacle
stations in the course stays constant for both groups.
I. I NTRODUCTION
Automated wheelchairs that are equipped with sensors and
a data processing unit constitute a special class of wheeled
mobile robots, termed smart wheelchairs in general literature
overviews [13], [5]. Beside general scientific fields of work,
such as autonomous navigation approaches, or mapping and
self-localization algorithms, the shared spatial reference system between the operator and the smart wheelchair gives rise
to certain issues related to user interfaces and shared control
problems. For instance, Simpson et al. showed in [15], [14]
how to combine discrete driving commands coming from
voice control with navigation assistance provided by reactive
navigation approaches.
The work presented in this paper is a follow-up of research
we conducted in [12], where we evaluated a component for
wheelchair driving assistance with healthy subjects, i.e. students. The main contributions of this paper are experiments
with real wheelchair users, in a standardized setting, and the
introduction of a measure for the complexity of control tasks,
i.e. the entropy rate of the commands given to control the
wheelchair.
This work has been funded by the Deutsche Forschungsgemeinschaft in
the context of the SFB/TR8 “Spatial Cognition”, project T2-[Rolland].
C. Mandel is with the Department of Mathematics and Computer Science
- FB3, University of Bremen, Enrique-Schmidt-Str. 5, 28359 Bremen,
Germany [email protected]
T. Röfer is with the German Research Center for Artificial Intelligence,
Safe and Secure Cognitive Systems Group, Enrique-Schmidt-Str. 5, 28359
Bremen, Germany [email protected]
I. Lohmüller is with the Neurological Rehabilitation Center
Stiftung Friedehorst, Rotdornallee 64, 28717 Bremen, Germany
[email protected]
Fig. 1. A subject is navigating an L-shaped ramp by use of the wireless
head-joystick cap and the driving assistance.
The paper is structured as follows: first, we give an
overview of the automated wheelchair. Afterwards, the driving assistance module is briefly described. Since we tested it
with two different input devices, a short review of special input devices follows. Then the experimental setup is described
together with the evaluation criteria used. Afterwards, the
results are discussed. The paper closes with our conclusions
of the findings.
II. S YSTEM OVERVIEW
The experimental platform is based on the power
wheelchair Xeno (cf. Fig. 1) by the German manufacturer
Otto Bock Healthcare. The wheelchair has its drive wheels
in the back and castor wheels in the front. The specialty
of that wheelchair model is the active castor wheels. They
are always rotated by motors to the orientation that matches
the current driving direction. Hence, all the problems that
normally occur with passive castor wheels in wheelchairs,
such as blocking wheels after a change of the driving
direction, are solved in this model. The wheelchair offers
a CANBUS interface that allows wire-tapping between the
joystick and the motor control.
The Xeno was extended by two laser range sensors (model
S300 by Sick), one in the front behind the foot rests, the other
one in the back. They measure the distance to the closest
obstacles in a height of 12 cm above the ground and have
an opening angle of 270∘ . The drive wheels were equipped
with wheel encoders with a resolution of approximately
2 mm driving distance per tick. A micro controller board
is counting the encoder ticks. The micro controller, a USB-
right wheel speed
left wheel speed
Fig. 2.
Some examples of safety regions for different driving directions.
CANBUS adapter, and both laser range sensors (via USBRS422 converters) are connected to a USB hub. A netbook
class PC is controlling the system through a single USB cable
connected to the hub.
A. Driving Assistance
The basic idea of the driving assistance module (the
driving assistant) is to detour obstacles in a way that is most
likely to be acceptable for the user. By taking into account
the desired traveling direction in terms of the curve indicated
by the user via the joystick or another device that gives
similar directions, the assistant decides whether to avoid an
obstacle, and if yes, to which side, i.e. to the right or to the
left. The driving assistant controls both speed and steering
of the wheelchair. The speed is always reduced in a way
that the wheelchair cannot collide with the obstacles in the
environment. The steering is controlled to avoid obstacles
to the side the user intends to, or not to avoid an obstacle
if the user directly heads toward it. In the latter case, the
wheelchair would simply stop to prevent a collision.
The wheelchair has a rather complex shape and the shape
of the area it “touches” during a braking maneuver is even
more complex. To effectively avoid obstacles, the knowledge
about this safety area is pertinent. The safety area depends on
dynamic and static parameters. The dynamic parameters are
the current translational and rotational driving speeds, and
their expected change due to the commands previously sent
to the wheelchair. The static parameters are the maximum
errors expected in measuring the dynamic parameters, the
shape of the actual wheelchair, the latency with which it executes commands, its deceleration during braking maneuvers,
and the behavior of the steering while braking. In case of
the Xeno, the latter is rather interesting, because the internal
motor control of the wheelchair always turns back the active
castor wheels to “straight ahead” when a full stop command
is issued. Thereby, the Xeno breaks out of the curve it was
previously driving while braking. Since the computation of
the safety area is rather complex, a large number of safety
Fig. 3. First described in [10], [9] and [7], the head-joystick is mounted
at the back of the user’s head and outputs the posture of the sensor’s local
coordinate system S w.r.t. a fixed global coordinate system G. The pitch
and roll deflections are subsequently translated into steering commands.
areas have been pre-computed and stored in a table. Some
examples of these areas are shown in Figure 2.
To determine the avoidance maneuver that matches the
intention of the human driver best, the direction he or she
indicates with the joystick is considered. All the time, the
driving assistant searches the local environment for obstacles
based on this direction employing the corresponding safety
region, i.e. the region that the wheelchair will reach within
the next few seconds. If the wheelchair is already detouring,
the direction indicated by the user may deviate from the
system’s current steering direction. Thus, the driving assistant always assesses the world from the user’s point of view,
and this view includes the judgment whether an obstacle
is on the left or on the right side of the intended driving
direction (indicated by the colors green and red in Fig. 2).
The algorithms employed are detailed in [12].
B. User Interfaces
Traditional automated wheelchairs are operated by joystick, directly translating the user’s hand movements into
translational and rotational velocities. While such interfaces
suit a large audience, certain disabilities may require appropriate alternatives, e.g. Brain-Computer-Interfaces ([11],
[4]), head posture [1] or gaze [3] interpretation, and natural
language communication ([6], [8]), to name but a few.
With regard to people needing specialized input devices
due to handicaps such as tetraplegia, this study analyzes the
impact of the proposed driving assistance module (cf. Sec. IIA) on a common hand-operated joystick and a head-joystick
(cf. the cap in Fig. 1), first described in [10], [9], and [7].
While people are familiar with the use of the former device,
i.e. the deflection of the joystick maps proportional onto the
direction and the velocities of the wheelchair, the later one
is more sophisticated. The basic idea is to let the user of
a power wheelchair control the translational and rotational
velocity by continuous pitch and roll movements of his/her
head (cf. Fig. 3). These movements are measured by an
inertial measurement unit (IMU) and translated into steering
commands.
III. E VALUATION
The central motivation behind the clinical evaluation of
the driving assistant is to judge whether or not it is able
to improve the quality of life of the users. For this reason,
ten subjects who require a wheelchair in their daily life
have been asked to navigate Rolland on a benchmarking
wheelchair parcours.
A. Setup
The standardized course had been designed by the Spanish
Sport Federation for people with Cerebral Palsy (FEDPC)
[2]. It includes five obstacle stations mimicking common
obstructions for wheelchair-bounded persons. All obstacles
have been set up inside an inner courtyard at the neurological rehabilitation center Stiftung Friedehorst. They were
encompassed by a dedicated start and finish line. The first
obstacle “square of 360∘ turn” consists of a square area
with 1.2𝑚 side length, marked by four pylons standing in
its corners. Subjects were asked to enter the obstacle area
through the front side, make a 360∘ turn inside the obstacle
region, and leave the obstacle through the opposing side. The
second obstacle “figure of eight” is given by three pylons
placed in line with 1.2𝑚 distance to each other. The task
was to perform the slalom-like obstacle on an eight-shaped
trajectory. The third obstacle “inclines with a turn” consists
of three square parts with 1.2𝑚 side length each, linked to a
L-shaped compound (cf. Fig. 1). After the subjects entered
the obstacle over the first part, i.e. a 15𝑐𝑚 high upward
ramp, they had to make a 90∘ turn to the right on the leveled
part of the obstacle, before leaving the obstacle on the final
downward ramp. The fourth obstacle station “square for turn
of 180∘ ” is given by an additional square region of 1.2𝑚
side length, marked by pylons in its corners. In contrast to
obstacle 1, the subjects had to enter the obstacle through the
two front pylons, make a 180∘ turn inside the obstacle, and
leave backwards between the two opposing pylons. The fifth
and final obstacle station “inversion door” only consists of
two pylons, placed in line with a distance of 1.2𝑚 to each
other. As with the exit gate of obstacle 4, the subjects had
to traverse this door backwards.
Figures 1 and 4 show parts of the parcours, while Figures 6(a)–6(d) depict the whole parcours, starting in the
upper left corner and following the obstacles in clockwise
direction.
B. Selection of Subjects
The experimental evaluation of the driving assistant module comprises two groups of five subjects each. While participants from group A performed their test-runs by support
of the driving assistant, participants from control group
B performed without. Both groups have been composed
Fig. 4.
Wheelchair parcours standardized by the FEDPC (cf. Sec. III-A).
of participants suffering from comparable functional and
cognitive impairments.
Group A involved three men and two women aged 2772. They were all experienced wheelchair users (from four
months up to 35 years), while three of them could look back
on several years of using a power wheelchair. Four out of the
five subjects showed symptoms of an acquired neurological
disease, which had led to a partial loss of the capability to
stand or walk within the past five years. Two of the five
subjects showed tetra spasticity accentuated in the lower
limbs, two more suffered from hemiparesis, and one from
progressive muscle impairment (cf. group A participants in
Table I for details).
Group B consisted of three women and two men between 17 and 50 years of age, who were also experienced
wheelchair drivers (between two months and seven years).
As in group A, these participants also showed symptoms of
acquired neurological diseases, which had led to a partial
loss of their capability to stand or walk. As in group A, two
showed signs of tetra spasticity, two more of hemiparesis,
and one suffered from ataxic apraxia, i.e. movement disorder
(cf. group B participants in Table I for details).
C. Procedure
In order to determine the effect of smart driving assistance
on the participants’ ability to navigate within the described
course, each of the ten subjects (group A with driving
assistance, group B without) was scheduled for five sessions
of maximal 60 minutes. In each of the five sessions he/she
was asked to complete the course beginning from the start
line, getting through the different obstacle stations, and
finishing at the goal line. After the first half of a single
session, subjects changed from navigating by using the
hand-joystick to navigation by head-joystick. This strategy
allowed for a differentiated view on both modalities w.r.t.
their adequacy given the specific clinical pattern of each
single participant. Even though the participants of the study
have been encouraged to complete all obstacle stations while
pursuing good lap times, obstacles could be aborted or
neglected when they imposed intractable problems.
TABLE I
G ENDER , AGE , WHEELCHAIR EXPERIENCE , AND FUNCTIONAL IMPAIRMENTS OF THE 10 SUBJECTS FROM GROUPS A AND B.
subject
A1
A2
A3
A4
A5
B1
B2
B3
B4
B5
gender
♀
age
27
experience
-
♂
62
43 years
♂
72
4 years
♀
51
6 years
♂
53
–
♀
52
1 year
♀
♀
22
22
1 year
2 months
♂
17
1 year
♀
23
7 years
motoric impairments
spastic hemiparesis left, reduced flexibility of left
shoulder, unstable gait
spastic movement patterns in all four limbs, reduced
ability to stand
reduced ability to stand and walk, significantly reduced muscular strength and endurance
reduced ability to stand and walk, significantly reduced muscular strength and endurance
reduced strength in dorsal flexor of the left foot,
significantly reduced ability of left arm movement
paraplegia with inability to stand and walk, torso
instability
slight symptoms of hemiparesis right
missing control over left knee, reduced strength in
dorsal flexor of left foot, reduced strength in left hand
fracture of right shoulder resulting in limited movement
ataxia with risk of fall, slowdown of all motoric
actions
During the test runs, the actual controller input
(𝑠𝑝𝑒𝑒𝑑, 𝑠𝑡𝑒𝑒𝑟𝑖𝑛𝑔), and the estimated global pose (𝑥, 𝑦, 𝜃)
was logged. The later was computed by the application
of a GMapping implementation from OpenSLAM [16]. In
addition the advisor monitored the number of collisions,
as well as neglected, aborted, or falsely executed obstacle
stations.
D. Evaluation Criteria
In order to evaluate the impact of the driving assistant on
the subject’s ability to steer Rolland through the parcours,
logged data has been evaluated against several performance
criteria. By combining the average distance 𝑑[𝑚] each subject
required to complete the course, and the average elapsed time
𝑡[𝑠], we take the average velocity 𝑣[𝑚/𝑠] (calculated as root
mean square, RMS) as the first indicator for smooth and swift
trajectories. The second evaluation criterion, i.e. the average
number of collisions per lap 𝑐/𝑙, is a good indicator for the
subject’s precision in passing the different obstacle stations.
The third performance measure is given by the subject’s
average number of falsely executed, aborted, or disregarded
obstacles per lap 𝑒/𝑙. As the average number of collisions
per lap, this indicator gives strong evidence on the subject’s
ability to master the challenges of the obstacle course.
In contrast to the first three criteria given above, the fourth
performance criterion gives more abstract evidence on the
level of difficulty imposed by the test runs with and without
driving assistance. This criterion is the entropy rate 𝐻 of the
controller histograms integrated over the subject’s test runs:
𝑛
𝐻𝑟𝑎𝑡𝑒 = −
1∑
𝑝(𝑥𝑖 ) log∥𝑋∥ 𝑝(𝑥𝑖 )
𝑛 𝑖=1
(1)
The entropy rate of a single controller histogram (cf. Fig.
6(e) - 6(h)) can be interpreted as a measure of clustering
of all speed and steering pairs (𝑣, 𝜔) commanded by a
single subject during all of his/her test runs. It is assumed
that a lower entropy rate, i.e. a higher clustering, indicates
cognitive and sensorial impairments
spatial neglect left, retarded
–
glasses and acoustic instrument
–
slightly reduced response times, slight memory problems, impairment of visual perception
–
slightly reduced response times
reduced proprioceptive sensibility and haptic perception in upper and lower extremities
dizziness
impaired visual perception, slight memory problems,
reduced attention and concentration capabilities
TABLE II
C OMPLETED LAPS ( CL ), NUMBER OF COLLISIONS ( NOC ), NUMBER OF
FALSELY EXECUTED OBSTACLES ( FEO ), AND ENTROPY RATE ( ER ).
VALUE PAIRS ( X / Y ) CORRESPOND TO TEST RUNS CONDUCTED BY
HAND - JOYSTICK / HEAD - JOYSTICK .
subject
CL
NOC
FEO
ER
subject
CL
NOC
FEO
ER
A1
15/1
1/–
20/3
4.27/1.29
B1
7/7
2/15
2/1
3.8/1.22
A2
15/1
–/–
8/9
2.69/1.27
B2
6/2
4/29
–/–
3.86/1.25
A3
9/7
–/4
2/14
1.71/1.12
B3
4/4
2/29
1/5
5.21/1.18
A4
7/8
–/–
1/4
1.86/1.21
B4
2/2
1/18
2/5
2.98/1.12
A5
8/13
1/4
3/6
2.86/1.19
B5
10/4
79/83
15/12
3.84/1.22
less cognitive challenges since fewer changes of speed and
direction are involved. It is worth mentioning that entropy
rates for controller histograms are only comparable if they
belong to the same class of input device, i.e. hand-joystick
or head-joystick. The reason for this is the difference in
the domains of 𝑣 and 𝜔, yielding different alphabets 𝑋 in
equation (1).
IV. R ESULTS AND D ISCUSSION
A. Findings
Taking all valid laps accomplished by subjects of experiment 1 into account, i.e. laps in which I) each of the five
obstacle stations has at least been passed by in the correct
order, and that II) completely connected the start line and
the finish line, results given in Fig. 5 indicate first insights.
In the following, all given significance values result from the
Student’s t-test with nine degrees of freedom.
Subjects in group A, i.e. performing with driving assistance, achieved RMS speed values of 0.18𝑚/𝑠 with handjoystick, and 0.14𝑚/𝑠 with head-joystick respectively. Subjects in group B who performed without driving assistance
130
1000
120
900
110
800
100
driven distance [m]
80
600
70
500
60
400
50
40
driven time [s]
700
90
300
30
200
20
100
10
0
0
A,1
A,2
A,3
A,4
A,5
B,1
B,2
participant [group,number]
B,3
B,4
B,5
Fig. 5. Comparison of basic performance criteria, i.e. time of travel and
driven distance for the ten subjects. RMS values include data from valid
test runs accomplished by each participant during all of his/her five training
sessions. A single test run has been judged as valid if each of the five
obstacle stations has at least been passed by in the correct order, and if
the final trajectory completely connected the start line and the finish line.
Group A performed the course by support of the driving assistant, while
group B did without. Red and blue boxes represent data recorded during
test runs operated by hand-joystick, while green and pink boxes illustrate
data coming from test runs operated by head-joystick.
achieved RMS speed values of 0.21𝑚/𝑠 with hand-joystick,
and 0.18𝑚/𝑠 with head-joystick. These results display an
average speed being 17% (hand-joystick) and 29% (headjoystick) higher when performing without driving assistant
compared to runs with driving assistant. Significance is given
by 𝑝 = .75 when comparing RMS speed values achieved by
hand-joystick, and 𝑝 = .8 for head-joystick values.
In terms of average number of collisions / valid lap (𝑐/𝑙)
group A participants caused 0.04𝑐/𝑙 when performing with
hand-joystick, and 0.27𝑐/𝑙 with head-joystick respectively.
Group B subjects that performed without driving assistant
caused 3.07𝑐/𝑙 when using the hand-joystick, and 9.37𝑐/𝑙
when using the head-joystick (cf. Table II). Significance is
given by 𝑝 = .77 when comparing 𝑐/𝑙-values achieved by
hand-joystick, and 𝑝 = .99 for head-joystick values. It must
be mentioned that non-zero 𝑐/𝑙 values for test runs with
driving assistance occurred in two situations. First, when
a subject halted during his/her test run for a longer period
of time, the driving assistant intentionally forgets obstacles
that it cannot perceive anymore. Although this behavior is
appropriate in cases where regions have been seen a long
time ago, i.e. one can make only weak assumptions at present
whether this region is still occupied or not, it poses the
threat of potential collisions within the blind sensor areas
when moving on. The second situation in which collisions
happened was during light rain. The precise reasons for these
problems are still under investigation, but it may have to do
with the laser scanners used not being intended for outdoor
usage.
A third criterium for the assessment of the driving assistant
is given by the average number of falsely executed obstacles
/ valid lap (𝑒/𝑙). Group A participants showed an average
of 0.63𝑒/𝑙 when performing with hand-joystick, and 1.2𝑒/𝑙
when performing with head-joystick respectively. Participants from group B showed an average of 0.69𝑒/𝑙 when
driving with hand-joystick, and 1.21𝑒/𝑙 when driving with
head-joystick (cf. Table II). Significance is given by 𝑝 = .19
when comparing 𝑒/𝑙-values achieved by hand-joystick, and
𝑝 = .64 for head-joystick values.
With respect to the entropy rate of each subject’s controller
histogram, participants who controlled the wheelchair by
hand-joystick scored an RMS value of 2.83 ⋅ 10−3 with
support of the driving assistant (group A), while participants
who controlled the wheelchair by hand-joystick without
support of the driving assistant (group B) scored an RMS
value of 4.0 ⋅ 10−3 . When the same subjects controlled the
wheelchair by use of the head-joystick, they achieved RMS
values of 1.21 ⋅ 10−4 (group A), and 1.20 ⋅ 10−4 (group
B) respectively. Significance is given by 𝑝 = .94 when
comparing RMS entropy rates achieved by hand-joystick,
and 𝑝 = .36 for head-joystick values.
B. Conclusion
In this work we have presented the driving assistant
as a software artifact that supports the user of a power
wheelchair in common navigation scenarios. Demonstrated
by the application of a common hand-joystick and an IMUbased head-joystick, the automated correction of insecure
driving commands applies to a wide range of possible user
interfaces that place the operator in the control loop. With
an extensive evaluation of a clinical study that involved ten
subjects navigating Rolland through a standardized course,
the main contribution of this paper is the comparison of
subjects performing with driving assistance to a control
group driving without, given several performance criteria.
It shows that the driving assistant successfully reduces the
number of collisions nearly down to zero. An unfavorable
effect of the driving assistant can be seen in a reduced
average speed of travel, as well as an increased trajectory
length. The average number of falsely executed obstacles
equals in both groups and is unaffected by the application
of the driving assistant. Here subjects performing with headjoystick caused twice as many errors as when performing
with hand-joystick. A further observation explains at least
for test runs conducted by hand-joystick the increase of
errors when driving without assistance, in terms of a higher
level of cognitive challenges, i.e. increasing entropy rates of
the resulting controller histograms. In contrast, controlling
the wheelchair with the head-joystick appears to be equally
complex with and without assistance, maybe because it is so
unfamiliar to experienced wheelchair user.
ACKNOWLEDGMENT
The authors would like to thank all participants for their
efforts and patience during the experimental evaluation.
Additional thanks go to C. Pfaab for his organisatory support
during the conducted test runs.
Session 1
Session 2
Session 3
Session 4
Session 5
no Session 1
Session 2
Session 3
Session 4
no Session 5
-5
-5
[m]
0
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0
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-15
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[m]
10
15
[m]
(a) A3: hand-joystick trajectories
(b) B1: hand-joystick trajectories
Session 1
Session 2
Session 3
Session 4
Session 5
no Session 1
Session 2
Session 3
Session 4
no Session 5
-5
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[m]
0
[m]
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[m]
(c) A3: head-joystick trajectories
1
0.025
0.0007
0.02
0.4
0.2
0.015
0
-0.2
0.01
-0.4
-0.6
0.005
normalized controller input: speed
normalized controller input: speed
0.0006
0.6
0.0005
0.4
0.2
0.0004
0
0.0003
-0.2
-0.4
0.0002
-0.6
0.0001
-0.8
-0.8
-1
0
-1
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-0.4
-0.2
0
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normalized controller input: steering
0.6
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1
(e) A3: hand-joystick histogram
-1
0
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
normalized controller input: steering
0.6
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(f) A3: head-joystick histogram
normalized controller input: speed
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15
(d) B1: head-joystick trajectories
1
0.8
10
[m]
1
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(g) B1: hand-joystick histogram
1
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-5
0.0008
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0
0.0004
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normalized controller input: steering
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(h) B1: head-joystick histogram
Fig. 6. Illustration of trajectories and controller histograms (cf. Sec. III-D) for two exemplary subjects performing their test runs with hand-joystick and
head-joystick. A3 conducted the test runs with support of the driving assistant module, while B1 did without.
R EFERENCES
[1] M. Bureau, J.M. Azkoitia, G. Ezmendi, I. Manterola, and H. Zabaleta.
Non-invasive, wireless and universal interface for the control of
peripheral devices by means of head movements. In Proceedings of
the 10th IEEE Intl. Conf. on Rehabilitation Robotics (ICORR), 2007.
[2] Federacion Espanola de Deportes de Paraliticos Cerebrales.
Reglamento De Slalom En silla De Ruedas 2009-2012, 2010.
[3] J. Gips. On building intelligence into EagleEyes. In Assistive
Technology and Artificial Intelligence, Applications in Robotics, User
Interfaces and Natural Language Processing, pages 50–58. SpringerVerlag, 1998.
[4] B. Graimann, B. Allison, C. Mandel, T. Lueth, D. Valbuena,
and A. Grser. Robust Intelligent Systems, chapter Non-invasive
Brain-Computer Interfaces for Semi-Autonomous Assistive Devices.
Springer Verlag, 2009.
[5] A. Lankenau and T. Röfer. Smart wheelchairs - state of the art in
an emerging market. Künstliche Intelligenz. Schwerpunkt Autonome
Mobile Systeme, 4:37–39, 2000.
[6] S.P. Levine, D.A. Bell, L.A. Jaros, R.C. Simpson, Y. Koren, and
Borenstein J. The NavChair assistive wheelchair navigation system.
IEEE Transactions on Rehab. Engineering, 7(4):443–451, Dec. 1999.
[7] C. Mandel and U. Frese. Comparison of wheelchair user interfaces for
the paralysed: Head-joystick vs. verbal path selection from an offered
route-set. In Proceedings of the 3rd European Conference on Mobile
Robots (ECMR 2007), 2007.
[8] C. Mandel, U. Frese, and T. Röfer. Robot navigation based on the
mapping of coarse qualitative route descriptions to route graphs. In
Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2006), pages 205–210, 2006.
[9] C. Mandel, U. Frese, and T. Röfer. Design improvements for
proportional control of autonomous wheelchairs via 3DOF orientation
tracker. In Proceedings of the 9th International Work-Conference on
Artificial Neural Networks (IWANN’2007), Lecture Notes in Computer
Science. Springer; Berlin, 2007.
[10] C. Mandel, T. Röfer, and U. Frese. Applying a 3DOF orientation
tracker as a human-robot interface for autonomous wheelchairs. In
Proc. of the 10th Intl. Conference on Rehabilitation Robotics, 2007.
[11] B. Rebsamen, E. Burdet, C. Guan, H. Zhang, C. Leong Teo, M. Ang,
and C. Laugier. Controlling a wheelchair using a BCI with low
information transfer rate. In Proceedings of the 10th IEEE Intl. Conf.
on Rehabilitation Robotics (ICORR), 2007.
[12] T. Röfer, C. Mandel, and T. Laue. Controlling an automated
wheelchair via joystick/head-joystick supported by smart driving assistance. In Proceedings of the 2009 IEEE 11th International Conference
on Rehabilitation Robotics, pages 743–748, 2009.
[13] R. C. Simpson. Smart wheelchairs: A literature review. Journal of
Rehabilitation Research & Development, 42(4):423–436, 2005.
[14] R.C. Simpson and S.P. Levine. Voice control of a powered wheelchair.
IEEE Transactions on Neural Systems and Rehabilitation Engineering,
10(2):122–125, June 2002.
[15] R.C. Simpson, S.P. Levine, D.A. Bell, L.A. Jaros, Y. Koren, and
J. Borenstein. Lecture Notes in Computer Science: Assistive Technology and Artificial Intelligence, chapter NavChair: An assistive
wheelchair navigation system with automatic adaption, pages 235–
255. Springer Berling/Heidelberg, 1998.
[16] C. Stachniss, U. Frese, and G. Grisetti. OpenSLAM - Give your
algorithm to the community, 2010.