Biomechanics and Neural Control of Movement

Transcription

Biomechanics and Neural Control of Movement 2016
Deer Creek Conference Center and Lodge, Mt. Sterling, OH
(www.deercreekstateparklodge.com)
June 12-17, 2016
Meeting Chair: Daniel Ferris (University of Michigan)
Program Committee: Yasin Dhaher (University of Northwestern), Daniel Ferris (University of
Michigan), Fran Gavelli (NIH), Rachael Seidler (University of Michigan), Doug Weber
(DARPA), Paul Zehr (University of Victoria)
Biomechanics and Neural Control of Movement – Conference Agenda
Deer Creek Lodge and Conference Center, Sterling, OH
(http://www.deercreekstateparklodge.com/)
June 12-17, 2016
Meeting Chair: Daniel Ferris (Michigan)
Program Committee: Yasin Dhaher (Northwestern), Daniel Ferris (Michigan), Fran Gavelli (NIH), Rachael Seidler
(Michigan), Doug Weber (DARPA), Paul Zehr (Victoria)
Sunday, June 12, 2016
3:00 pm – 6:00 pm
Registration (Front Lobby)
6:00 pm – 7:30 pm
Barbecue Cookout Dinner with Open Bar (Shelter House)
7:30 pm – 9:00 pm
Reception with Open Bar (Shelter House)
Monday, June 13, 2016
7:00 am – 8:30 am
Breakfast (Greater Mezzanine)
8:45 am – 9:00 am
Welcome and Introduction (Grand Ballroom)
Dan Ferris, Meeting Chair
9:00 am – 12 noon
Session 1: 20 Years Later: What Have We Learned and What Has Changed? (Grand
Ballroom)
Chair/Discussant: Fay Horak (OHSU); Speakers: Zev Rymer (Northwestern), Andy
Biewener (Harvard), Andy Schwartz (U of Pittsburgh), Daofen Chen (NIH)
12:15 pm – 1:30 pm
Lunch (Greater Mezzanine)
1:30 pm – 4:00 pm
Ad hoc discussions/free time
4:30 pm – 5:30 pm
Poster session with cash bar (Grand Ballroom)
5:30 pm – 6:45 pm
Dinner (Greater Mezzanine)
7:00 pm – 10:00 pm
Session 2: Muscle as an Actuator: Mechanics, Energetics, and Plasticity (Grand Ballroom)
Chair/Discussant: Walter Herzog (Calgary). Speakers: Rick Lieber (Northwestern),
Sabrina Lee (Northwestern), Silvia Blemker (Virginia), Tom Roberts (Brown)
10:00 pm – 11:00 pm
Poster Session with Social Hour (Grand Ballroom)
Tuesday, June 14, 2016
7:15 am – 8:45 am
9:00 am – 12:00 noon Session 3: Skeletal Structure as Framework and Limitation in Health and Disease (Grand
Ballroom)
Chair/Discussant: Fran Gavelli (NIH); Speakers: Elizabeth Brainerd (Brown), Karen Troy
(WPI), Sandra Shefelbine (Northeastern), Janet Ronsky (Calgary)
12:15 pm – 1:30 pm
1:30 pm – 4:00 pm
4:30 pm – 5:30 pm
5:30 pm – 6:45 pm
7:00 pm – 10:00 pm
Session 4: Rhythmic Movements in Natural and Artificial Systems
Chair/Discussant: Young-Hui Chang (Georgia Tech); Speakers: Art Kuo (Michigan), Vivian
Mushahwar (Alberta), Monica Daley (RVC), Max Donelan (Simon Fraser)
10:00 pm – 11:00 pm
Wednesday, June 15, 2016
7:15 am – 8:45 am
9:00 am – 12:00 noon Session 5: Biological and Artificial Reach and Grasp (Grand Ballroom)
Chair/Discussant: Francisco Valero-Cuevas (USC); Speakers: Tamar Flash (Weizmann
Institute), Stephen Scott (Queens), Aaron Dollar (Yale), Marco Santello (Arizona State)
12:15 pm – 1:30 pm
1:30 pm – 4:00 pm
4:30 pm – 5:30 pm
5:30 pm – 6:45 pm
7:00 pm – 10:00 pm
Session 6: Neuromotor Adaptation and Learning (Grand Ballroom)
Chair/Discussant: Sandro Mussa-Ivaldi (Northwestern); Speakers: Rachael Seidler
(Michigan), Richard Carson (Trinity College Dublin), Maurice Smith (Harvard), Yasin
Dhaher (Northwestern)
10:00 pm – 11:00 pm
Thursday, June 16, 2016
7:15 am – 8:45 am
9:00 am – 12:00 noon Session 7: Neuro-Musculoskeletal Models: Can We Simulate Realistically?
Chair/Discussant: Wendy Murray (Northwestern); Speakers: CJ Heckman
(Northwestern), Mitra Hartmann (Northwestern), Allison Arnold (Harvard), Brian
Umberger (U Massachusetts Amherst)
12:15 pm – 1:30 pm
1:30 pm – 4:00 pm
4:30 pm – 5:30 pm
5:30 pm – 6:45 pm
7:00 pm – 10:00 pm
Session 8: Optimizing Human-Machine Interaction in Health and Rehabilitation
Chair/Discussant: Jim Patton (UIC); Speakers: Neville Hogan (MIT), Kat Steele
(Washington), David Reinkensmeyer (UC Irvine), Rob Riener (ETH)
10:00 pm – 11:00 pm
Friday, June 17, 2016
7:15 am – 8:45 am
9:00 am – 12:00 am
Session 9: 20 Years From Now: What Will Be Different and What Will We Know? (Grand
Ballroom)
Chair/Discussant: Gerald Loeb (USC); Speakers: Kiisa Nishikawa (Northern Arizona), Rick
Neptune (Texas), Alexander Leonessa (NSF), Doug Weber (DARPA)
12:00 noon
Box lunch to go (Ballroom Foyer)
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Oral Presentation
Sessions
1
Session 1: 20 Years Later: What Have We Learned and What Has Changed?
Fay Horak ([email protected]), 2Zev Rymer, 3Andy Biewener, 4Andrew Schwartz, and 5Daofen Chen
1
Oregon Health Sciences University, Portland, OR, USA
2
Northwestern University, Evanston, IL, USA
3
Harvard University, Boston, MA, USA
4
University of Pittsburgh, Pittsburgh, PA, USA
5
National Institutes of Health, Bethesda, MD, USA
The scientific focus of research on Biomechanics and Motor Control of Movement has changed over the last
several decades since the last meeting. For example, our understanding of posture control has changed from the
framework of functional stretch reflexes with fixed synergies from studies in a few subjects to a framework of
skilled set of sensorimotor skills with flexible synergies that can be adaptively trained. This symposium will
review the origins and impact of these types of transitions, and highlight promising new directions that have
emerged, in parallel with the development of advanced new techniques. The examples of change presented by
each speaker will begin a springboard of discussion for the meeting to consider what we have learned and where
we want our field of research to go.
Zev Rymer will discuss the shift in research on spinal cord regulation of motor control. Before 1990, there
were many research laboratories working on the physiology and pathophysiology of muscle receptors. Now
there are no NIH funded projects addressing these systems. There was also active research on efferent
innervation of muscle receptors, and on the related regulation of muscle spindle behavior during movement in
human and animal preparations. Currently, there is almost no ongoing work in this field. In addition, our
understanding of spinal interneuron circuitry at the time was quite primitive and classification of mammalian
spinal interneurons was quite arbitrary and inconsistent. Today, new genetic and molecular approaches are
providing more useful frameworks for understanding spinal and supraspinal physiology and function.
Andy Biewener will talk about how our understanding of muscle function in vivo has changed over the last 20
years. Skeletal muscles throughout the animal kingdom have highly conserved features at the molecular and
myofilament levels, providing generally similar mechanical and energetic properties. However, skeletal muscles
can also differ considerably in fiber type and architectural design (pinnation angle, fiber length, and connective
tissue organization). This greatly changes how they contract to neural activation and in vivo length change.
Since 1996, a great deal of new studies have been conducted on different animal species to examine in vivo
muscle mechanics. These studies have greatly improved our insight into the variety of functions that muscles
serve to power animal movement.
Andrew Schwartz will present his perspective on cortical control of arm and hand movements. At the last
meeting, he presented results that showed how arm trajectories could be decoded from the firing rates of motor
cortical neurons. One of the take-home messages was that detailed movement information was present in a
population of these cells that could not be easily extracted from single-unit firing rates. Shortly thereafter, it was
possible to simultaneously record action potentials from large groups of motor cortical cells to decode
movement in real-time with chronic microelectrode arrays. This started the field of neural prosthetics, and
today, paralyzed human subjects are using this technology to perform near-natural movements of the arm and
hand to carry out a variety tasks. It is expected that with increasing technological development, that neural
prosthetics will reach wide clinical utility.
Daofen Chen will talk about the evolution of NIH funding in the last twenty years. He will discuss how the
types of funded projects in motor control have changed from 1996 to 2016.
Session 2: Muscle as an Actuator: Mechanics, Energetics, and Plasticity
Walter Herzog ([email protected]), 2Rick Lieber, 3Tom Roberts, 2Sabrina Lee, and 4Silvia Blemker
1
University of Calgary, Calgary, Alberta, Canada
2
3
Brown University, Providence, RI, USA
4
University of Virginia, Charlottesville, VA, USA
1
In this session, we will introduce and discuss the structure, function and properties of skeletal muscles in the
context of in vivo force production and movement control. This discussion will range from basic mechanisms of
muscle contraction to in vivo muscle properties and function, and strategies of recruitment. The plasticity of
muscle properties, and mechanisms driving plasticity, will be addressed in the context of training, disuse, aging
and muscular and neuromuscular injuries and diseases. After Walter Herzog’s introductory discussion of the
topic, each of the speakers will touch on the following aspects of muscle.
Lieber section: Using intraoperative laser diffraction, we have measured sarcomere length in human muscles.
We routinely measure sarcomere lengths on the descending limb of the human length-tension curve, which is
troubling in light of modern-take muscle mechanics theory. In light of these findings, it is also extremely
provocative that we measure extremely long sarcomere lengths in vivo (i.e. >4.0 µm) in the same wrist muscles
of children with cerebral palsy. These results suggest that the sarcomere length operating range is
“programmed” into specific muscle groups and can be disrupted by disease.
Roberts section: Any model of the neural control of movement must address a central question: how is wellcoordinated movement achieved through an actuator that exhibits a mechanically complex behavior? We can
work from an understanding of the behavior of sarcomeres and well-characterized sarcomere properties.
However, work on whole muscle function in vivo indicates that this leaves out important determinants of force
production. The behavior of elastic elements within the muscle extracellular matrix and in series with muscles,
influences the speed and force of contraction which must be accounted for by the motor control system.
Lee section: Muscle properties are altered in individuals with neurological and motor impairments. However,
non-invasive measurements of material properties has been limited. We use ultrasound shear wave elastography
to quantify muscle properties in individuals with neurological impairments such as stroke and cerebral palsy, we
found differences in shear wave velocity between the paretic and non-paretic side. These results suggest that
muscle stiffness is indeed affected by muscle length, activation, and neurological impairments such as stroke
and cerebral palsy.
Blemker section: Considering the feedback between movement and muscle adaptation is critical to
understanding neuromuscular control and pathology. We are developing a modeling framework for integrating
muscle biomechanical properties and cellular behaviors to predict muscle adaptation to use, disuse, injury, and
neuromuscular disease. The ability to investigate the interaction between these phenomena empowers us to
develop a deeper understanding the complex mechanisms behind muscle impairments in neuromuscular
diseases as well as to develop novel treatment strategies.
Session 3: Skeletal Structure as Framework and Limitation in Health and Disease
1
Fran T. Sheehan ([email protected]), 2Elizabeth Brainerd, 3Karen Troy, 4Sandra
Shefelbine, and 5Janet Ronsky
1
National Institutes of Health, Bethesda, MD, USA
2
Brown University, Providence, RI, USA
3
Worcester Polytechnic Institute, Worcester, MA, USA
4
Northeastern University, Boston, MA, USA
5
University of Calgary, Calgary, AB, Canada
Although all functional movement is born out of the interplay between the neurological, skeletal, and muscular
systems, it is the skeletal system that forms with basic framework from which functional movement is created.
The skeletal system is also the end effector for the motor control and muscular systems. Too often the activities
of daily living produce movement patterns and tissue loads that exceed the basic capabilities of the skeletal system
or the muscle and motor control systems that coordinates it. The result can be profound impairments, functional
limitations and ultimately physical disabilities, greatly impacting our society in terms of direct costs and the
individual in terms of quality of life. Thus, central to understanding human neuromuscular development, along
with the genesis of musculoskeletal pathologies, is an understanding of how the human skeletal system adapts
and mal-adapts to the stresses places on it. Importantly, these adaptation are both static (e.g., bone shape, size) as
well as dynamic (e.g., joint movement) in nature.
The study of the animal skeletal system has and continues to provide relevant insights into the human system.
The systematic study of evolution has highlighted the key environmental and internal stressors that have guided
the development of the modern human skeletal system. Also, animal studies often enable the quantification of
certain key properties that cannot be evaluated in humans. Lastly, and potentially most importantly, the animal
model enables the evaluation of skeletal adaptation to controlled stimulus and targeted pathological changes.
Bones are particularly responsive to loading during growth. This plasticity in the pediatric skeleton can result in
bone deformities under altered loading (such as developmental dysplasia of the hip and scoliosis), but also offers
the potential to prevent deformities by ensuring the appropriate mechanical environment. Various experimental
and computational approaches have used to understand growing bone’s sensitivity to the mechanical environment.
In particular by combining motion capture techniques, musculoskeletal modeling, and finite element modeling
tissue level stimuli can be determined from whole body movement. A better understanding of how tissue level
stresses and strain are altered in pathologic cases will guide prevention or rehabilitation strategies.
Until recently, most kinematic and kinetic properties of the human musculoskeletal system could not be measured
directly without the use of invasive techniques. With the recent development of a host of imaging techniques
(e.g., dynamic MRI, bi-plane and single-plane fluoroscopy, ultrasound), our understanding of the interplay of the
motor control, muscular, and skeletal system is rapidly expanding. New multi-modal imaging approaches enable
novel non-invasive insights into in-vivo skeletal system interactions These approaches are allowing research into
healthy human skeletal joint and movement status during aging, as well as evaluation of joint injuries, treatments
and rehabilitation protocols during critical movements. Combined with computational models of in vivo function,
these imaging tools are providing new opportunities for advancing knowledge and developing tools for improving
quality of life for those with mobility impairments.
Acknowledgments:
(Author 1) Intramural Research Program of the National Institutes of Health Clinical Center, Bethesda, MD,
USA. (Author 5) Natural Sciences and Engineering Research Council of Canada (NSERC), Alberta Innovates
Technology Futures (AITF)
Session 4: Rhythmic Movements in Natural and Artificial Systems
1
Young-Hui Chang ([email protected]), 2Monica A. Daley, 3Max Donelan,
4
Arthur D. Kuo and 5Vivian K. Mushahwar
1
2
Georgia Institute of Technology, Atlanta, GA, USA
Royal Veterinary College, University of London, London, GBR
3
Simon Fraser Univeristy, Burnaby, BC, CAN
4
University of Michigan, Ann Arbor, MI, USA
5
University of Alberta, Edmonton, AB, CAN
Rhythmic movements are intrinsic to the daily repertoire of all biological systems, including the ability to
sustain prolonged locomotor behavior. Locomotion requires an organism to exhibit stable and consistent
movements as it navigates through and interacts with an unpredictable physical environment. The neural control
of locomotion further relies on a centrally generated intrinsic rhythmic pattern of muscle activations in addition
to descending command inputs and modulation from peripheral sensory signals. A basic understanding of
locomotion can present unique challenges to scientists studying independently within biomechanics or
neuroscience. What is required to advance our knowledge of locomotor control, however, is the development of
fundamental principles integrating biomechanics and neural control. To address this challenge we have relied
on and will continue to require transdisciplinary approaches, collaborations and efforts across numerous fields
that include physiology, biomechanics, neuroscience, computer science, robotics, engineering, and others.
There have been significant advancements in our understanding, beginning with simple biomechanical
templates that could explain and predict the basic dynamics of locomotion. Subsequent developments have
included theoretical frameworks that incorporate models of neural circuitry, how the complexity of constituent
parts is organized to accommodate perturbations and non-steady conditions, and how sensory information is
integrated to produce and adapt locomotion to result in an energetically efficient and economical gait. Our
current scientific understanding is being incorporated into robots and devices that can emulate the biological
system, augment performance, and test our understanding. This session will explore the interaction and
integration of biomechanical and neural mechanisms of locomotor control from several of these perspectives.
Young-Hui Chang will present findings of how and which biomechanical parameters may be represented within
the mammalian nervous system for locomotor control. Moreover, he will discuss how these neuromechanical
representations are influenced by natural variability, injury, and adapt under novel conditions.
Monica Daley will present evidence from perturbation experiments and reduced-order models that reveal
neuromechanical strategies for robust, stable, and economical locomotion. She will deliberate on the
interactions between biomechanics and sensory systems (i.e., vision, proprioception), to inform how sensory
contributes to perturbation recovery.
Max Donelan will discuss his findings on instantaneous energetics and optimization processes involved in
legged locomotion. He will discuss governing rules for continuous optimization of energy cost and how this
may be accomplished in human walking.
Art Kuo will provide his observations on how the dynamics of the biomechanical system influence the energetic
cost of locomotion and ultimately its control. He will discuss how using models, robots and human experiments
can inform our understanding of legged locomotor control and impact human health and rehabilitation.
Vivian Mushahwar will discuss how we can directly interface with the nervous system using engineered devices
to regain coordinated movement and locomotor control. In particular, she will examine how the integration of
biomechanics and neurophysiology can lead to solutions for augmenting the control of movement.
1
Session 5: Biological and Artificial Reach and Grasp
Francisco Valero-Cuevas ([email protected]), 2Tamar Flash, 3Stephen Scott, 4Aaron Dollar, and 5Marco Santello
1
University of Southern California, Los Angeles, CA, USA
2
Weizmann Institute, Rehovot, Israel
3
Queen’s University, Kingston, Canada
4
Yale University, New Haven, USA
5
Arizona State University, Tempe, USA
Today, as in the past, reach, grasp and manipulation continue to both fascinate and challenge neuroscientist (who
want to understand and rehabilitate it), and roboticists (who want to achieve it). It is now clear—with the
advantage of hindsight—that in 1996 we had somehow missed critical elements and perspectives that have seen
much development in the past 20 years. In spite of these advances, there still remain multiple challenges before
we can confidently deploy our knowledge to transform the clinical understanding and treatment of upper
extremity function, or manufacture robots that approximate our manipulation abilities in unstructured
environments. In this session we will attempt to do a debriefing about how:
•
•
•
•
•
Compliant manipulators in other species point to severe simplifications in our thinking about what
“manipulation” is (Flash).
The role of on-line, sensory-driven control is much more sophisticated and necessary than we suspected
(Scott).
How, conversely, under-actuation and embedded logic in the anatomy also play an important role as coevolution of the brain and hand that are critical to ecological versatility; and how it is important to
distinguish between grasp and manipulation (Dollar).
Learning, sensorimotor adaptation, and memory are critical contributors that integrate these different
mechanisms across objects and time (Santello).
It may well be the case that musculature is not “redundant” for real-world tasks that naturally have multiple
spatio-temporal constrains. This compels us to revise our notions of optimization and synergies.
Additionally, studies of dynamic manipulation at the edge of instability confront us with the need for an
integrative cortico-spino-muscular perspective to biological and robotic manipulation (Valero-Cuevas).
Our intent is to promote, and carry out frank and open-minded discussions so that we can encourage and guide
the community to make progress based on these valuable advances.
1
Session 6: Neuromotor Adaptation and Learning
Sandro Mussa-Ivaldi ([email protected]), 2Richard Carson, 3Maurice Smith, 4Rachael Seidler, and
1
Yasin Dhaher
1
2
Trinity College Dublin, Ireland
3
Harvard University, Cambridge, MA, USA
4
The last three decades have witnessed a profound change in our perspectives on motor learning. As classically
conceived, the study of motor skill acquisition dealt with the processes through which we develop and refine the
ability to perform some set of actions. On the other hand, motor adaptation came to be seen as "parameter
change" following exposure to some sensory motor perturbation. The discussants in this session have
contributed to a recasting of these notional distinctions by advancing the view that through skill acquisition and
adaptation the motor system acquires actionable knowledge about the environment in which it operates. With
this perspective, the conceptual boundaries between implicit and explicit, procedural and declarative, cognitive
and motor have been reappraised. Importantly, in this new framework sensory and motor noise have become
key elements in the shaping and selection of actions and in the development of internal representations. New
experimental techniques have evolved based on the controlled interaction with actual or simulated mechanical
environments and the mathematical tools have evolved to include increasingly state estimation as well as linear
and nonlinear dynamical modeling. Informed by related developments in cognate fields, there has also been
renewed consideration of the contribution of specific cellular processes to different aspects of motor learning.
The discussion will focus on these new views of learning and adaptation, highlighting key open issues, such as
the interactions between spatial and temporal features, the role of uncertainty, the relation between feedforward
and feedback mechanisms, the relative role of uncertainty and expectation in the temporal course of learning,
and the clinical impact of our understanding of learning and adaptation with respect to the development of new
approaches to the treatment of motor disabilities. Consideration will also be given to means by which genetic
and epigenetic analyses may inform our understanding of these factors, advance our understanding of the
cellular processes that mediate neuromotor adaptation and learning, and suggest new approaches to the
remediation of motor function.
Session 7: Neuro-Musculoskeletal Models: Can We Simulate Realistically?
Wendy Murray ([email protected]), 1CJ Heckman, 1Mitra Hartmann, 2Allison Arnold-Rife, and
3
Brian Umberger
1
2
Harvard University, Cambridge, MA, USA
3
University of Massachusetts, Amherst, MA, USA
1
One of the critical limitations of neuro-musculoskeletal models as research tools is the seemingly ubiquitous
skepticism about their validity and, therefore, about the conclusions that are derived from simulations. Each of
the four invited speakers will highlight a unique issue in neuro-musculoskeletal modeling. We expect a vibrant
discussion that highlights how: (i) effective simulation techniques, (ii) coping with the limitations of
simulations that lack realism, and (iii) the constant mission to validate this research methodology have all
advanced our understanding of the neuro-musculoskeletal system.
CJ Heckman will discuss the use of realistic computer simulations of the neuromodulatory actions of serotonin
(5HT) and norepinephrine (NE) implemented within a systemic effort to reverse engineer the firing patterns of
human motor units. Simulations of the neurotransmitter systems that act on G-protein coupled receptors in
motoneurons have provided a means to understand the excitability of motoneurons, i.e. their "state". Via
comparisons with detailed surface array recordings of motor unit firing patterns, simulation methods have
enabled identification of the relationship between the temporal pattern of EMG and the temporal pattern of all 3
components of motor commands (excitation, inhibition, and neuromodulatory state). This work has important
implications for understanding to what degree EMG patterns reflect motor command patterns.
Mitra Hartmann will highlight that we cannot yet perform realistic simulations of muscle-driven whisker
(vibrissae) actuation. The rat vibrissal system, which is used by the animal to tactually explore their
environment by actively brushing and tapping against objects (“whisking”) is often used to study somatosensory
processing and active touch. Whiskers have no sensors along their length; all sensing is performed within a
densely innervated follicle at the whisker base. The study of whisker actuation is in its infancy: only within the
past five years have the relevant muscles been identified. Steps required to develop realistic simulations will be
identified. In the absence of realistic neuromuscular models, results of simulations that exploit whisking
kinematics to study the statistics of active tactile sensing will be described.
Allison Arnold-Rife will summarize ongoing work to test the predictions of Hill-type muscle models within
simulations of human and animal movements. Independent measurements are needed to test and refine model
predictions during submaximal, dynamic contractions; however, obtaining such measurements and making
rigorous comparisons poses several challenges. This talk will review approaches for evaluating and refining
muscle models. Ultimately, this work will be critical for translating multi-body, muscle-driven simulations of
movement into clinical practice and treatment.
Brian Umberger will discuss the development of accurate models of the energetics of muscle force and work
generation, and – in doing so – will reiterate a common problem: model development is often hampered by a
relative lack of data on which to base parameter identification and model validation. He will summarize the
implementation of magnetic resonance spectroscopy to directly measure the in vivo cost of contraction during
volitional muscle activation in human subjects. Quantifying muscle energy consumption under a wide range of
experimental paradigms will provide the basis for creating more accurate models of muscle energetics, and
eventually deeper insights on the interrelationships among the mechanics, energetics and control of movement.
Session 8: Optimizing Human-Machine Interaction in Health and Rehabilitation
Jim Patton ([email protected]), 2Neville Hogan, 3Kat Steele, 4David Reinkensmeyer, and 5Robert Riener
1
University of Illinois, Chicago, IL, USA
2
MIT, Cambridge, MA, USA
3
University of Washington, Seattle, WA, USA
4
University of California, Irvine, CA, USA
5
ETH Zurich and University of Zurich, Switzerland
1
Robotic and other forms of interactive training and assistance devices for human movement have increasingly
become more commonplace in research laboratories around the world. However, clinically and commercially
they still have a long way to go to being major successes. As a field, we must determine the best methods to
optimize robotic devices to improve movement and performance. Even for unimpaired individuals, we struggle
to predict how a given individual will adapt or respond to forces, torques and interactive dynamics applied to the
body. In other words, the software for this promising suite of hardware is a subject of great exploration. For
individuals with unique neurologic injuries, such as in stroke or spinal cord injury, we must further determine
how learning, adaptation, and recovery all might impact the design of human-machine interactions for
rehabilitation.
Jim Patton will introduce the current state of the art, its limitations and promise in the field.
Neville Hogan will trace a short history, and then discuss the application of human-interactive robotic
technologies to fundamental studies of neuromotor performance and their translation to therapy and assistance.
Kat Steele will present her latest work combining rapid prototyping, ultrasound imaging, synergy analysis, and
musculoskeletal simulation to evaluate and predict the impact of orthoses and assistive technology for individuals
with neurologic injuries.
David Reinkensmeyer will review several results from clinical testing of robotic therapy devices, robot-assisted
motor learning studies, and the emerging field of computational neurorehabilitation that suggest the beginnings
of a framework for predicting optimal device designs.
Robert Riener will show how the human can interact with the therapy robot using multimodal cues to optimize
rehabilitation.
The combined discussion at the end of the session will focus on how to move the field forward so it can realize
the potential to assist human movement in health and rehabilitation.
Session 9: 20 Years From Now: What Will Be Different and What Will We Know?
1
G. Loeb ([email protected]), 2K. Nishikawa, 3R. Neptune, 4D. Weber, and 5A. Leonessa,
1
University of Southern California, Los Angeles, CA, USA
2
Northern Arizona University, Flagstaff, AZ, USA
3
University of Texas, Austin, TX, USA
4
5
National Science Foundation, USA
This session is intended to provide an open discussion forum to consider how we got to the present, where we
would like to be in the future, and what obstacles we must overcome to get there.
Jerry Loeb will consider actual scientific progress in terms of reductionism vs. systems understanding.
We have highly disciplined methods to develop and validate new tools that have broad applicability (e.g. patch
clamps, genetic engineering and protein structural analysis) and to apply them to reductionistic understanding of
tiny parts of systems (e.g. how motoneurons are recruited and what holds myofilaments together). We have
comparatively few tools and little discipline when it comes to systems level understanding or clinical
interventions. Meanwhile, the next generation of methodologists is busily applying genomics, proteomics,
optogenetics, etc. to provide amazing details about the machinery that underlies physiological phenomena that
have mystified us. In 20 years, we are going to know even more about even less. Systems integration will be
even more difficult as its practitioners struggle to absorb this exponential growth of detail in each subsystem.
Rick Neptune will extrapolate from advances in musculoskeletal modeling and simulation of human
movement. Much has been done over the last 20 years, but there are formidable challenges ahead (e.g.,
integrating subject-specific parameters, models of the control system and sensory feedback; predictive muscle
models; validation; computational speed; actually impacting clinical practice etc.). Modeling and simulation is a
powerful tool to analyze human movement, but much remains to be done to advance the field and have a
clinical impact in patient populations.
Kiisa Nishikawa will discuss scientific inertia, the tendency for dogma to be accepted uncritically while
new ideas meet with inordinate resistance. Except for new ideas that result from technical advances, most new
ideas come from outsiders who think about long-standing problems in new ways. Leaders in a field typically
resist the participation of outsiders, slowing intellectual progress. Rather than perpetuating this pattern, the
scientific community should work energetically to decrease this inertia. Some ideas include journals devoted to
encouraging speculative work, as well as allowing more speculation in traditional venues. While science must
of course have some stability, the current climate is far too conservative. Within 20 years, we will have a
predictive model of muscle force that will enable advances in our basic understanding of motor control, as well
as applications in wearable robotics.
Doug Weber will extrapolate from the development and commercial success of intelligent prosthetic
legs that mimic biomechanics and reflexes, hopefully leading to direct, two-way communication between
prosthetic limbs and the nervous system that will enable volitional control and restored sensation for prostheses.
While challenges remain, ongoing efforts are aimed at creating permanent neural interfaces that are safe,
effective, and reliable enough for human use. To this end, mathematical models of the biomechanics and neural
control of limb function will be important for creating algorithms to decode myoelectric control signals from
muscles and for patterning electrical stimulation of sensory nerves to restore sensation and facilitate natural
reflex functions.
Alexander Leonessa will discuss how intelligent robots can assist people. Ambient intelligence,
ubiquitous and networked robots, and cloud robotics are new, hot research topics that aim to achieve semantic
perception, reasoning, and actuation. Ubiquitous robots integrated with web services could provide physical and
virtual companions to assist, protect and rescue people in indoor and outdoor spaces. Although it is easy to
imagine robots performing these tasks, many challenges needs to be solved in order to make this a reality.
Poster Presentation
Abstracts
1
Effort minimization predicts ankle over hip strategies
M. Afschrift ([email protected]), 1I. Jonkers and 1F. De Groote
1
KU Leuven, Leuven, Belgium
Experimental studies showed that a continuum of ankle and hip strategies is used to restore posture. Postural
responses can be modeled by feedback control, with feedback gains that optimize a specific objective [1]. On
the one hand, feedback gains that minimize effort have been used to predict muscle activity during perturbed
standing. On the other hand, hip and ankle strategies have been predicted by minimizing postural instability and
deviation from upright posture. But it remains unclear whether and how effort minimization influences the
selection of a specific postural response. We hypothesize that the relative importance of minimizing mechanical
effort versus postural instability influences the strategy used to restore upright posture.
Experiments and predictive simulations of the postural response following a backward support surface
translation were used to test this hypothesis. 10 healthy adults participated in the study. Full body kinematics
and ground reaction forces were measured and processed in OpenSim. Significant correlations were found
between the measured hip range of motion, characterizing a hip strategy, and the mechanical effort, metabolic
work and muscle activity obtained from the experimental data (R=0.81, R=0.71, R= 0.7, p<0.001). Predictive
simulations were used to establish a cause effect relationship between the relative importance of minimizing
mechanical effort versus instability and the postural response. Therefore, the feedback gains of a torque driven
double inverted pendulum model with full state feedback were optimized to minimize the weighted sum of (1)
postural instability and (2) mechanical work in response to a backward surface translation. Hip strategies were
predicted when minimizing postural instability was more important (higher weight) whereas ankle strategies
were predicted when minimizing mechanical work was more important (Fig. 1). Furthermore, there was a
significant positive correlation between the weight that predicts the measured postural response best and the
measured hip range of motion (R=0.70, p<0.001). Hence, the trade-off between effort and postural instability
minimization can explain the selection of a specific postural response in the continuum of potential ankle and
hip strategies.
Figure 1: Results of the predictive simulation for a typical measured (A) ankle strategy and (B) hip
strategy. The left and middle graphs show respectively the predicted ankle and hip joint angle as a function of
time for different weights W. The right graphs show the root mean square error between the experimental and
simulated kinematics as a function of the weights. The simulation that best predicts the measured kinematics is
highlighted with the red dotted line in the right graphs and as a bold line in the left and middle graphs.
References
Park S. (2004) Postural feedback response scale with biomechanical constraints. Exp. B. Res. 154:417–427.
1.
A Novel Framework for Optimizing Motor (Re)-learning with a Robotic Exoskeleton
Priyanshu Agarwal and Ashish D. Deshpande ([email protected])
The University of Texas at Austin, Austin, TX, USA
Conventional therapies for stroke rehabilitation have failed to provide reliable recovery and thus a majority of
subjects are left with severe impairments, unable to accomplish activities of daily living. A number of robots
have been developed to assist in the rehabilitation process, but the results with robots have been no better than
those achieved with manual therapy [1]. This is because the current robot-assisted therapy programs are based
on manual therapies and make limited use of the evidence-based understanding of motor learning and neurorehabilitation. A critical question to be answered to improve robotic rehabilitation is what is the optimal
rehabilitation environment for a subject that will facilitate maximum recovery during therapy.
Our idea is to first understand the key factors that affect motor learning and neuromuscular rehabilitation and
then incorporate those in the robot control algorithm to give rise to a rehabilitation environment that is
optimized for each subject and is adaptively tailored based on his or her performance and needs. ‘Challenge
Point Hypothesis’ and also experiments suggest that optimal learning occurs when the challenge is suited to the
participant proficiency [2]. Challenge in robotic rehabilitation has so far been modulated by adjusting the
amount of assistance provided by the robot during therapy. However, experiments show limited success of this
approach as just adjusting assistance may not be sufficient to affect
true recovery. Literature shows that task variability (Practice
variability hypothesis) and augmented feedback also improve motor
learning and therefore can be used to modulate challenge.
We present a framework for performance-based modulation of
challenge in this multi-dimensional space (task, assistance and
feedback) on motor learning and re-learning during rehabilitation. The
framework is designed around the idea of providing an optimum
rehabilitation environment to each subject by adapting the
environment variables to provide a challenge level commensurate
with the level of the skill of the subject. The rehabilitation
environment consists of a human subject performing a functional task
Figure 1: An overview of the proposed
with UT hand exoskeleton, while the framework provides some form
controls framework for robot-assisted
of feedback (e.g. verbal, visual, or auditory) (Fig. 1). The performance
rehabilitation.
on the task is assessed using measures that estimate the level of skill
of the subject. The framework consists of continuous adaptation along the following three dimensions based on
the performance of the subject on a functional task: i) task frequency and amplitude adaptation to introduce
sufficient variability in the task for keeping the task optimally challenging based on the skill level of the subject,
ii) assistance adaptation to provide a haptic guidance or an error augmentation training while smoothly
transiting between the two based on the subject’s skill level, and (iii) feedback adaptation to provide just the
right amount of feedback to avoid reliance on feedback and instead encourage motor adaptation and learning.
Our ongoing work focuses on testing hypotheses to examine the efficacy of this multi-modal challenge
modulation for different tasks.
References
1. Krakauer, J W (2015) The app. of mot. learn. to neurorehab. Oxford Textbook of Neurorehab.: 55.
2. Guadagnoli MA and Lee TD (2004) Challenge point: a frame. for con. J. Mot. Behav. 36(2):212–224.
Acknowledgments
Supported by NSF CNS-1135949 and NASA NNX12AM03G.
Neuromuscular characterization of abnormal coordination patterns for post-stroke stiff knee gait
Tunc Akbas ([email protected]), Richard R. Neptune and James Sulzer
Previous studies suggest that hip circumduction during gait after stroke compensates for lack of foot clearance,
which is often caused by weakness of knee flexors or excessive knee extensor activity. However, individuals
with stiff-knee gait (SKG) often have more complex, neurally-originated impairments including hemiparesis,
hyper-reflexive behaviors, and abnormal coordination. Despite a wide range of studies investigating SKG, there
is no clear model of how neural impairments manifest themselves in SKG, which prevents the determination of
the most beneficial therapy and assistive devices. Indeed, our previous research using exoskeletal knee flexion
perturbations during gait suggests that hip abduction in people with SKG may not originate from lack of foot
clearance, but rather abnormal coordination [1].
We hypothesized that the abnormal coordination pattern between knee flexion and hip abduction may originate
from a cross-planar reflex coupling between abductors and rectus femoris (RF). Furthermore, the knee flexion
perturbation may enhance the intensity of this coupling. As such, the activation of a specific abductor should
follow the increased stretch velocity of RF. Based on the experimentally collected kinetic, kinematic and EMG
data on nine SKG patients and five healthy controls, we used neuromusculoskeletal modeling to simulate
muscle activities and muscle fiber stretch velocities to help identify a reflex coupling in a dynamic environment.
(for detailed methods, see [2]). We searched for reflex couplings by comparing the peak RF stretch velocities to
the peak abductor (gluteus medius (GMED), tensor fasciae latae (TFL) and gluteus maximus (GMAX)) activity
for each step of each individual. Linear mixed-effects analysis was used to determine whether these muscles
were active along with increased RF stretch velocity (α<0.05). In addition, we extracted the reflex latency
between the peak RF velocity and the peak abductor activation.
Figure 1: Rectus femoris (RF) peak stretch velocity and integrated
gluteus maximus (GMAX) muscle activity around peak values (±
4% gait cycle) in individuals with SKG and healthy controls with
and without flexion perturbations. Linear regression indicates that
there is a positive correlation between RF stretch velocity and
GMAX activity for SKG (r = 0.54, p < 0.05), which does not exist
in healthy controls (r = -0.15, p = 0.13).
We found a correlation between increased RF
stretch velocity and GMAX activity (p<0.05) for
people with SKG compared to healthy controls,
whereas no correlations were found in the
abductors, GMED (p=0.26) and TFL (p=0.71).
This is indicative of a specific coupling between
RF and GMAX (Figure 1). Of the individual
steps with the greatest coupling, i.e. high RF
stretch velocity (>0.5) and high GMAX
activation (>0.4), latency between peaks
averaged 85 ms, indicative of a heteronymous
reflex latency. These results suggest the existence
of a previously unknown abnormal reflex
coupling existing during gait following stroke.
This information further characterizes the
complexity of impairments following stroke and
could be used to predict patient response to
therapy and exoskeletal assistance.
References
Sulzer JS, et al. Stroke 41.8, 1709-14, 2010.
Akbas, T, Sulzer, J. ASB 2015.
1.
2.
Evaluating the Effects of Gait Rehabilitation on Post-Stroke Muscle Coordination
Jessica L. Allen ([email protected]), Trisha M. Kesar, and Lena H. Ting
Emory University, Atlanta, GA, USA
Muscle coordination is commonly impaired post-stroke [1], but the magnitude and pattern of impairments in
muscle coordination can vary across individuals, and may contribute to the variability in patient response to
rehabilitation interventions. FastFES, a gait rehabilitation intervention combining fast treadmill training and
functional electrical stimulation (FES), was designed to specifically target the deficits associated with abnormal
plantarflexor activation during post-stroke gait. While 12-weeks of FastFES training has been shown to improve
gait function, there is considerable inter-subject variability in response to the FastFES treatment, which is not
fully explained by clinical or biomechanical measures of impairment [2].
Here, we present a case-series demonstrating that differential effects of FastFES on gait function in a responder
and non-responder may be associated with differences in muscle coordination impairments prior to treatment.
We used motor module analysis [3] to identify different patterns of muscle dyscoordination that can affect gait
in post-stroke hemiparesis. Based on these preliminary results in 2 stroke survivors, we hypothesize that: 1)
FastFES training can ameliorate specific muscle coordination deficits in a subpopulation of individuals with
post-stroke hemiparesis; and 2) baseline muscle coordination deficits can serve as additional predictors of
response to post-stroke gait training.
Two individuals greater than six months post-stroke completed a FastFES training program consisting of 18
sessions (2-3 sessions/week). Improvements in gait function were assessed using timed-up-and-go (TUG) and
six-minute walk test (6MWT). Each participant also completed electromyography (EMG) testing pre- and posttraining. Participants walked overground at self-selected walking speed while EMG data were collected from 13
paretic leg muscles. Motor modules were identified from EMG using non-negative matrix factorization [3].
Our results provide evidence that different types of abnormal plantarflexor recruitment may respond differently
to FastFES. Based on clinical scores, one participant was labeled a responder (TUG: 6.5 to 5.5s; 6MWT: 520.6
to 580.3m) and the other a non-responder (TUG: 24.8 to 31.7s; 6MWT: 164.9 to 139.1m). Each participant
initially had different patterns of abnormal plantarflexor recruitment. In the responder, a motor module was
identified pre-training with abnormal plantarflexor/dorsiflexor co-activation that was successfully unmerged
with FastFES. In contrast, the non-responder initially presented with a motor module having abnormal
plantarflexor/knee extensor co-activation that was not altered with FastFES.
Understanding the causes of inter-individual variability in responsiveness to an intervention is a key question
that, if addressed, may enable improvements in walking function and quality to be maximized at discharge from
rehabilitation. Baseline muscle coordination may be an additional factor, on top of clinical and biomechanical
measures, to examine contributors to inter-individual variability in responsiveness to gait rehabilitation.
References
1. Knutsson E and Richards C (1979). Different types of disturbed motor control in gait of hemiparetic
patients. Brain 102:405-30.
2. Awad LN et al., (2014). Targeting paretic propulsion to improve walking function: a preliminary study.
Arch Phys Med Rehabil 5:840-8.
3. Chvatal SA and Ting LH (2013). Common muscle synergies for balance and walking. Front in Comput
Neurosci 7:48.
Acknowledgements
Supported by NIH grants R01-HD46922, 1F32-NS087775, K01-HD079584, and AHA grant SDG 13320000.
Exaggerated Dorsiflexor Excitability: A Biomarker for Gait Impairment Following Stroke?
Caitlin L. Banks ([email protected]), 1Virginia L. Little, 1Eric R. Walker, and 1,2Carolynn Patten
1
Malcom Randall VAMC, Gainesville, FL, USA
2
University of Florida, Gainesville, FL, USA
1,2
Ankle plantarflexion is critical to production of forward propulsion, momentum, and limb advancement during
the swing phase of gait. Many individuals experience deficits in plantarflexor power generation following
stroke, however the underlying mechanism is poorly understood [1]. Paradoxically, many strategies for gait
rehabilitation following stroke target so-called foot-drop or dorsiflexor dysfunction. While investigating neuromotor mechanisms during isolated plantarflexion (PF) tasks, we observed increased corticospinal excitability to
the tibialis anterior (TA) leading us to hypothesize that exaggerated activation of the antagonist TA muscle
during PF contributes to decreased ankle power during walking after stroke.
13 individuals post-stroke (age 63±8 years, chronicity 7±6 years, 11 male) and 10 healthy Controls (age 61±9
years, 5 male), participated in neurophysiological and biomechanical testing on separate days. We applied
transcranial magnetic stimulation during isometric and dynamic PF and investigated modulation of TA motor
evoked response area (MEParea) between conditions. Positive TA MEParea change indicates increased excitation
during dynamic, relative to isometric, PF. Ankle PF power was measured during instrumented gait analysis.
! !" Fig. 1 illustrates inverse correlation between the magnitude of TA MEParea facilitation during dynamic PF and
ankle PF power during walking post-stroke. TA MEParea facilitation during dynamic PF reveals two nonoverlapping sub-groups in stroke. Importantly, TA MEParea is not significantly facilitated during dynamic PF in
healthy individuals.
"!
"!
Figure 1: TA MEParea change reveals a significant
negative correlation with peak concentric ankle power (A2)
in individuals post-stroke (r = -0.66, orange).
Exaggerated facilitation of TA motor response during PF is pathologic. We propose this dysregulation of
dorsiflexor excitability represents a biomarker of functional impairment relevant to walking post-stroke. Our
results suggest the locus of underlying neurophysiological impairment may involve the reciprocal inhibition and
the transcortical reflex pathways, both of which are mutable. Elucidating and differentiating these mechanisms
of motor impairment will identify novel treatment targets and improve the efficacy of neurorehabilitation.
References
1. Jonkers I, Delp S, Patten C (2007) Capacity to increase walking speed is limited by impaired hip and ankle
power generation in lower functioning persons post-stroke. Gait Posture 29:129–137.
Acknowledgments
Supported by the Department of Veterans Affairs, Rehabilitation RR&D Service (Grant #O1435-P and
Research Career Scientist Award 3F7823S, Patten, PI), University of Florida Graduate Student Assistantship
(Banks), VA Office of Academic Affairs Advanced Fellowship in Geriatrics (Little).
Biomechanical Structural Changes Impacts on the Claw Finger Deformity in the Intrinsic-Minus Hand
1,2,4
Benjamin I Binder-Markey ([email protected]), 1,2Julies PA Dewald and 1,2,3,4,5Wendy M Murray
Departments of 1Biomedical Engineering, 2Physical Therapy and Human Movement Sciences, 3Physical Medicine and
Rehabilitation, Northwestern University, Chicago, IL, USA
4
Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA
5
Edward Hines, Jr. VA Hospital, Hines, IL, USA
After an injury that paralyzes the intrinsic hand muscles (“intrinsic-minus” hand),
patients develop a claw finger deformity (hyperextension of the MCP and flexion of
the IP joints) when they attempt to extend their fingers (Figure 1) [1]. However, the
claw finger deformation is not always initially present and over time the deformity
develops and becomes more severe [1]. The progression of the deformity is
postulated to be due to secondary biomechanical structural changes that occur after Figure 1: Image of severe claw
paralysis of the muscles. These changes include: (i) increased joint laxity, (ii) finger deformation in the
stretching of extensor mechanism causing anterior displacement of the lateral slip, intrinsic-minus hand [1].
and (iii) shortening of the extrinsic finger flexor muscles [1]. We aim to explore how these changes impact the
claw finger deformity using a biomechanical musculoskeletal model to better inform rehabilitation methods.
A musculoskeletal biomechanical model of the intrinsic minus hand was used for this study [2-4]. Models
demonstrating varying levels of joint laxity, anterior slippage of the extensor mechanism, and shortening of the
extrinsic finger muscles, along with a combined model were developed to assess how each of biomechanical
structural changes affect the claw finger posture (Figure 2). A dynamic forward simulation of each model was
performed to demonstrate how each deficit contributes to the claw finger deformity. The wrist was set and
constrained to 30° of extension for the forward simulations and 30° of flexion for one simulation. Muscle
excitations were defined as 20% flexor activity to fully flex the finger then 20% extensor activity to extend the
finger, joint angles were recorded when extended equilibrium was reached.
The forward dynamic simulation in the intrinsic minus model mimics acute paralysis of the intrinsic muscles;
the claw deformity was not noticeably demonstrated (Figure 2). Simulation of the combined model with all
three secondary structural changes demonstrated a significant claw finger deformity, which disappeared when
the wrist was flexed (Figure 2). When assessing the individual changes, small shortening of the resting lengths
of the extrinsic finger flexors displayed the claw finger deformity and large decreases in the passive torques
resulted in a mild display of the claw deformity (Figure 2).
These results indicate that the claw finger deformity posture is
most sensitive to shortening of the extrinsic finger flexors and that
maintaining the length of the extrinsic finger flexors could be an
area of focus for rehabilitation interventions to help prevent the
claw finger deformity from progressing in acute injuries.
Additionally these models could be used to develop mechanical
devices or electrical stimulation protocols that prevent or reverse
the deformity.
REFERENCES
1.
2.
3.
4.
Schreuders, T.A.R., J.W. Brandsma, and H.J. Stam, Physikalische Medizin
Rehabilitationsmedizin Kurortmedizin, 2007. 17(1): p. 20-27.
McConville, J.T., et al., Anthropology Research Project, 1980. AFAMRL-TR-80-119.
Binder-Markey, B. and W.M. Murray, - In prep, 2016.
Saul, K.R., et al., Comput Methods Biomech Biomed Engin, 2015. 18(13): p. 1445-58.
ACKNOWLEDGEMENTS
NIH-NIBIB T32EB009406; NIH 1R01HD084009-01A1 Dewald/ Murray (PIs)
Figure 2: Results of the forward simulations at extension
equilibrium with images and joint angles.
1
Customized Therapy Using Distributions of Reaching Errors
Moria Fisher Bittmann ([email protected]), 2,3Felix C. Huang, and 2,4James L. Patton
1
University of Wisconsin, Madison, WI, USA
2
Rehabilitation Institute of Chicago, Chicago, IL, USA
3
Northwestern University, Chicago, IL, USA
4
University of Illinois at Chicago, Chicago, IL, USA
Introduction: While it is widely recognized that stroke survivors exhibit major differences in motor
performance, current methods for customizing rehabilitation have been limited. Our recent work suggests that
stroke survivor subjects exhibit patterns that can be uniquely identified [1]. Our latest work with healthy
participants showed that patterns of error exhibited during reaching trajectories can be used to construct
individualized force training environments that improved motor learning [2]. Here in a pilot study with three
stroke survivors, we asked whether such customized training environments might reduce reaching errors better
than practice alone.
Perpendicular Error (cm)
Methods: Three stroke survivor subjects (mean Fugl-Meyer score of 21±2) participated in this study at the
Rehabilitation Institute of Chicago (Chicago, IL). Participants manipulated a planar force feedback device to ten
target locations arranged in a pentagram pattern 18 cm apart. After completing each reach, they received
feedback of their movement time. Baseline reaches at the beginning of each training day (Fig 1A) served as the
basis for the design of customized forces, or error fields. Our design approach is intended to apply perturbing
forces during training according to both the magnitude and probability of the error. As such, we modeled the
probability using the error observed during baseline reaches (Fig 1B) by computing the mean and standard
deviation at each point along the ideal straight-line path. By design, the largest forces would occur when the
participant had high,
B 15
C 15
A
Fig. 1. A) Baseline reaches
consistent error (Fig
to ten target directions re10
10
1C). Participants first
aligned to their starting
5
points, B) Perpendicular
5
received one session
error
for
several
trajectories
of null field training
0
0
in one target direction, C)
(no forces) followed
−5
−5
Distribution of error (gray)
0
10
20
0
10
20
by five sessions of
with theoretical perturbing
Distance along
Distance along
error fields.
forces overlaid in red.
target path (cm)
target path (cm)
Results: We found that all participants significantly improved (decreased perpendicular error) between
initial baseline reaching to the final evaluation session. Our ANOVA results indicated that target direction and
subject were significant factors on error across sessions (p<0.001). The error decreased an average of 2.8 cm for
Participant 1 (40% error reduction, p=0.0152), 1.3 cm for Participant 2 (35% error reduction, p=0.0074), and
0.9 cm for Participant 3 (18% error reduction, p=0.0486). We found all participants significantly reduced error
from both the initial null field and subsequent error field training conditions. However, there were no detectable
differences in error drop between these two conditions (p>0.05).
Discussion and conclusions: Using error statistics we were able to focus on errors made most frequently
and ignore spurious or random errors. Here we showed early encouraging evidence of using an individual’s
tendencies of error to customize therapy where stroke survivor participants decreased reaching errors beyond
repetitive practice. This technique of intervening on errors that have high probability could serve as a basis for a
wide range of therapeutic and motor teaching approaches.
References
[1]
[2]
F. C. Huang and J. L. Patton, "Individual patterns of motor deficits evident in movement distribution analysis," IEEE ... International Conference on
Rehabilitation Robotics : [proceedings], pp. 1-6, 2013.
M. E. Fisher, F. C. Huang, V. Klamroth-Marganska, R. Riener, and J. L. Patton, "Haptic error fields for robotic training," in World Haptics Conference
(WHC), 2015 IEEE, 2015, pp. 434-439.
Supported by NIH R01NS05360
Altered lower limb standing coordination following stroke: Getting to the point
Wendy Boehm ([email protected]), Kreg Gruben
Humans use an impressively robust control solution to stand despite the significant mechanical complexity of
the task. Following stroke, however, hemiparesis interferes with typical motor control and manifests as poor
balance. The precise mechanism of that impairment has not been characterized despite extensive evidence of
atypical muscle coordination in the paretic (P) lower limb. The ground reaction force (F) is the output of that
coordination and drives whole body motion, motivating its measurement and comparison with non-paretic (NP)
individuals. P and NP F showed systematically altered behaviors, more precisely characterizing stroke-induced
miscoordination that predicts standing difficulties, compensations, and therapy objectives.
The sagittal-plane center of pressure (CP) and direction off vertical (θF) of F are of particular interest, because
the nervous system has the most latitude in adjusting these parameters to prevent falling over via the modulation
of whole-body angular momentum. Recent observations in non-disabled standing individuals exhibit a linear
relationship between CP and tan(θF) in the 2−7 Hz frequency band. That relationship geometrically represents
the F lines-of-action being directed through a fixed intersection point (IP), where the inverse of the CP vs
tan(θF) slope is IP height. To study this coordination, F was measured in quietly standing humans with and
without hemiparesis.
A line captured most of the CP vs tan(θF) variance (variance accounted
for: control 88%, non-paretic 98%, paretic 93%). The IP height was
near the CM (just above the hip) for the control participants, however
the NP leg exhibited a higher IP and the P leg a lower IP (Fig. 1).
2
IP height (fraction of hip height)
Six control (4 female, age 20−53yrs) and 3 chronic post-stroke (2
female, age 57−78yrs, 2 right-sided paresis) participants stood quietly
with a custom 6-axis force platform under each foot. Across 11
sessions on separate days for the post-stroke participants, and an
individual session for each control subject, F was recorded at 100Hz
for 15s. Signals were filtered with a 2nd order zero-lag Butterworth
filter at 2Hz high-pass and then 7Hz low-pass. The principal
component of the CP vs tan(θF) relationship determined the height of
the IP, which was expressed as fraction of hip height.
p < 0.00001
p < 0.00001
p < 0.00001
1
0
non-paretic
paretic
control-L control-R
Directing F below the CM in the P limb is remarkable, because it Figure 1: F during human standing
indicates a destabilizing coordination which would cause the body to is directed at a point (IP) at different
pitch away from upright.1 Increased height of the NP limb IP may heights in stroke and NP individuals.
provide enhanced stability to compensate for the P leg instability. The
paretic instability predicts the development of behaviors that avoid using this control for support as is
commonly observed after stroke2 (e.g. weight bearing asymmetry, knee hyperextension). The paretic IP near
knee height suggests that hip and knee torques are abnormally independent of ankle torque modulations.3
Rehabilitation focused on correcting these underlying control deficits is likely to have enhanced effectiveness.
References
1. Kumar, K. L. Engineering fluid mechanics. S. Chand, 2008.
2. Boehm, W. L., & Gruben, K. G. (2016). 7(1), 3-11.
3. Gruben, K. G., & Boehm, W. L. (2012). J Biomechanics, 45(9), 1661-1665.
Acknowledgments
Supported by the V. Horne Henry Fund, UW Graduate School, and the WI Alumni Research Foundation.
MASI: a novel Musculoskeletal model for the Analysis of Spinal Injuries
Cazzola D ([email protected]), 2Holsgrove TP, 1Preatoni E, 3Gill HS, and 1Trewartha G
1
University of Bath, Department for Health, Bath, UK
2
University of Pennsylvania, Spine Pain Research Lab, PA, USA
3
University of Bath, Centre for Orthopaedic Biomechanics, Department of Mechanical Engineering, Bath, UK
1
Cervical spine trauma from sport collisions or vehicle accidents can have devastating consequences for
individuals and a high societal cost. The precise mechanisms of such injuries are still unknown as investigation
is hampered by the difficulty in experimentally replicating the conditions under which these injuries occur.
We report on the creation and validation of a generic musculoskeletal model for the analyses of cervical spine
loading in healthy subjects. The novel improvements embedded in MASI consist of i) a scapula-clavicular joint
(SCJ) that provides the coupled motion of scapula and clavicle with respect to humeral elevation, ii) the inclusion
of body inertial parameters to permit dynamic analyses, and iii) an optimised scaling of neck muscles maximum
isometric force. The verification and validation procedures consisted of i) SCJ kinematic validation, ii) a dynamic
verification, and iii) a dynamic validation.
The ‘Musculoskeletal model for the Analysis of Spinal Injuries’ (MASI) was created in OpenSim 3.2 and Matlab
2013b software. MASI inherited the structure of the OpenSim head and neck model [1] which we embedded into
a full body model (OpenSim ‘2354’). Experimental data of full body kinematics (Oqus, Qualisys), ground reaction
forces (9287BA, Kistler), and neck muscles’ EMG (Delsys Trigno, DelsysInc) of a healthy male subject (age: 64
years, height: 1.67 m, mass: 75 kg) were collected during neck flexion, extension, lateral bending and axial
rotation movements. The SCJ kinematics throughout the humeral range of motion were within 2 standard
deviations (SD) of previous in vivo and in silico studies. The passive neck joint moments were comparable with
in vitro data (2 Nm) [2], and maximal net joint moments were comparable with healthy male subjects’ neck
strength (Ext: 50.8 Nm, Flex: 10.3 Nm, Lat Bend: 31.3 Nm, Ax Rot: 12.4 Nm). Finally, computed muscle control
simulations driven by in vivo neck kinematic data successfully simulated neck muscles’ activation (Fig. 1).
Figure 1: The simulated muscles (solid line) activation showed a similar pattern and activation level in
comparison with the recorded EMGs (dashed line) across the neck movements.
The implementation of MASI for the analysis of dynamic loading experienced in both sporting and occupational
activities will provide a greater understanding of the underlying mechanisms of cervical spine injuries.
References
1. Vasavada AN (1998) Influence of muscle morphometry and moment arms on the moment-generating capacity
of human neck muscles. Spine 23:412-422.
2. Miura T (2002) A method to simulate in vivo cervical spine kinematics using in vitro compressive preload.
Spine 27:43-48
Acknowledgments
This project is funded by the Rugby Football Union (RFU) Injured Players Foundation.
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Low-Dimensional EMG-Driven Musculoskeletal Model for Predicting Hand Kinematics
Dustin Crouch ([email protected]) and He Huang
1
North Carolina State University, Raleigh, NC, USA
Introduction: Many existing upper limb musculoskeletal models used for basic and clinical biomechanics research
have numerous elements (e.g. muscles, degrees of freedom (DOF)) to accurately reflect anatomy and make
physiologically-relevant predictions [e.g., 1]. Adapting such complex models for clinical applications, such as
real-time prosthesis control, requires more parameter adjustments and control signal (i.e. electromyography
(EMG)) measurements/estimates than is practical. Some low-dimensional (i.e. “lumped parameter”) models, with
fewer individual muscle elements and/or DOFs than the anatomical limb, have been used previously to make
reasonable biomechanical predictions for the wrist [2]. We developed and preliminarily evaluated a lowdimensional, 2-DOF musculoskeletal hand model to determine its ability to accurately predict movement
direction off-line, and (2) enable effective real-time control of a virtual hand during a path tracing task.
Methods: In separate trials, an able-bodied, right-handed male subject (age 31, height=178cm, weight=66kg)
performed self-selected wrist-only, metacarpophalangeal (MCP)-only, or simultaneous wrist-MCP movements,
with the arm in neutral posture and the elbow flexed to 90°. Normalized EMG measured from 4 muscles that
contribute to wrist and/or MCP joint movements (based on musculoskeletal geometry) were inputs to 4 respective
virtual muscles in a two DOF (wrist & MCP flexion/extension) planar hand model; select parameters were
computed by constrained numerical optimization (Matlab GlobalSearch function, Mathworks Inc., Natick, MA)
[3]. The model was implemented for real-time control of a 2-DOF virtual hand displayed on a computer screen;
2 joints were added distal to the MCP joint to increase the fingertip range of motion (Figure 1). The subject
attempted to trace straight and curved paths with the fingertip of the hand.
Results and Discussion: Over a 25-second continuous movement window, while moving either the wrist or MCP
joints independently, the model accurately predicted movement direction in 87% and 91% of timepoints at the
moving joint. When moving both joints simultaneously, the model accurately predicted wrist and MCP movement
direction in 78% and 86% of timepoints, respectively. Using the real-time model-based controller, the able-bodied
subject kept the fingertip to within a mean perpendicular distance of 4.8% and 5.5% (as a percentage of total hand
length) across straight and curved paths, respectively (Figure 1). In preliminary tests, a subject with transradial
amputation was able to trace some straight paths with more difficulty.
Figure 1: Fingertip trajectories (red line)
were reasonably close to target curved paths
(black line) during the tracing task (hollow
circles = start point).
While our initial results are promising, more work is needed
to demonstrate that the model can be easily adapted to and
effectively used by individual subjects, including those with
amputation, to perform real-world tasks. Additionally, since
model predictions strongly depend on the optimized parameter
values, we plan to explore other optimization algorithms and
evaluate the sensitivity of model predictions to parameter
variation. Given its relatively simple structure, our lowdimensional musculoskeletal model is more practical and
easily adaptable for widespread clinical translation, and our
modeling approach may be a powerful testbed for
implementing musculoskeletal models for other applications.
References
1. Holzbaur KR, et al. (2005), Ann Biomed Eng 33(6): 829-840.
2. Lehman SL and Calhoun BM (1990), Exp Brain Res 81:199-208.
3. Crouch DL and Huang H (2015), EMBC 2015, Milan, Italy. 1132-1135.
Acknowledgments
Supported by DHHS/NIDILRR #90IF0064, NSF #1527202, DOD #OR140147 & #13014002.
Dynamic Task Incentivization in a Robotic Haptic Cycle Ergometer
to Promote Neuroplastic Recovery after Stroke
Alexander R. Dawson-Elli ([email protected]) and Peter G. Adamczyk
University of Wisconsin-Madison, Madison, WI, USA
Robotic upper limb therapy has yielded substantial gains in motor recovery following stroke, while lower-body
rehabilitation exoskeletons have so far been less successful. Key features of successful rehabilitation are
thought to include high dosage, task-specificity, early enrollment, and volitional engagement with the task. We
speculate that current gait retraining approaches (e.g., body-weight-supported treadmill training, gait
rehabilitation exoskeletons, and split-belt training) excel at high dosage and task-specificity, but fall short in
early enrollment and volitional engagement. We further speculate that task-specific training can unintentionally
reward functional compensations instead of true neuroplastic recovery. We present here an alternative approach
to lower limb motor rehabilitation, with complementary strengths in comparison to those mentioned above.
We have recently begun development of a robotic rehabilitation cycle, in which crank speed or torque is
controlled in real-time to reward targeted motor patterns, determined through foot force and EMG coordination
(e.g. [1]). As above, high dosage can be delivered. Additionally, a recumbent cycle allows early enrollment, as
it does not require walking capacity. In contrast to past rehabilitation cycles [2], robotic haptic feedback can be
used to incentivize volitional submovements of cycling (e.g. leg retraction), and even motor patterns orthogonal
to the task (lateral foot force) or opposed to it (targeted eccentric phases). It is not task-specific, but is designed
to retrain flexible motor control through true neuroplastic recovery, not functional compensation.
Our first prototype cycle is under ongoing development. Fig. 1 shows data from two tasks: a constant resistance
torque and torque profile with a localized increase in resistance. These plots indicate the expected ability to
selectively influence motor patterns during a pedaling task, and represent initial progress toward rewarding
targeted tasks. Our goal for BANCOM is to show our progress in intervention design and data using this
approach, and improve the research program through interaction and discussion with the leaders in the field.
A
B
Figure 1: Example pedaling
speed vs. crank angle data
illustrating the influence of
haptic force control on
velocity pattern during two
pedaling tasks. During both
tasks, the subject attempted to
maintain constant speed
(black circle, 30 RPM). (A)
Constant resistance torque
control allows a subject to
gradually learn a constantspeed task. (B) A localized
resistance peak alters the
coordination pattern learned.
References:
[1] E. B. Brokaw (2013) Comparison of Joint Space and End Point Space Robotic Training Modalities for
Rehabilitation of Interjoint Coordination in Individuals With Moderate to Severe Impairment From Chronic
Stroke. IEEE TNSRE, 21(5):787–795.
[2] N. J. Hancock (2012) Effects of lower limb reciprocal pedalling exercise on motor function after stroke: a
systematic review of randomized and nonrandomized studies. Int. J. Stroke, 7(1):47–60.
Muscle Short-range Stiffness Explains Inverse Dynamics Joint Torques during Early Perturbed Standing
1
Friedl De Groote ([email protected]), 2Jessica L. Allen and 2Lena H. Ting
1
KU Leuven, Leuven, Belgium
2
Emory University and Georgia Institute of Technology, Atlanta, GA, USA
Muscle short-range stiffness (SRS) may be an important contributor to human postural control. SRS causes a
rapid rise in muscle force in response to an external stretch due to the deformation of engaged cross-bridges.
SRS allows muscles to resist external perturbations before spinal reflexes (latency of 50ms) or balancecorrecting responses (latency of 100ms) can intervene. SRS may be especially important in the biomechanics
and neural control of perturbed human balance but the Hill muscle model commonly used for dynamic
simulations of movement does not describe SRS. Here we augmented a Hill-type muscle model with a model of
SRS. We used this extended model to test whether SRS can explain inverse dynamics (ID) joint torques during
the initial response, i.e. the period in which no changes in muscle activity are observed, to support surface
translations in humans.
Two healthy subjects (S1: male, 34y; S2: male, 24y) were subjected to forward and backward ramp-and-hold
translations of the support surface. Marker coordinate data and ground reaction forces were collected and were
input to an inverse dynamics analysis. We computed constant muscle activations that were bounded between 0
and 0.3 and could account for the ID torques during the first 50ms and 100ms of the response. We used a
dynamic optimization approach to minimize the integral of the squared difference between muscle and ID
torques. Muscle dynamics was described by a Hill-type muscle model either with or without SRS.
In the first 50ms, including SRS improved the fit between muscle and ID torques for the knee and hip, where
changes in torque were large, but had little effect on the fit for the ankle, where the change in torque was small
(Fig. 1). Varying tendon stiffness had little influence on the root mean square error (RMSE) between muscle
and ID joint torques (Fig. 1). When increasing the interval of the simulations from 50 to 100ms, RMSE
increased, indicating that other mechanisms, likely muscle reflexes, play a role after 50ms.
A Hill-type muscle model extended with SRS can explain the inverse dynamic joint torques during the first
50ms of the response to support surface translations whereas a Hill-type muscle model without SRS cannot.
This work suggests that musculoskeletal models of perturbed balance, a common experimental paradigm for
investigating balance disorders, should include SRS to accurately represent the neural and biomechanical
factors important in human balance.
Acknowledgments
We gratefully acknowledge the support of NIH HD046922.
Figure 1 Left: Comparison of RMSE between muscle and ID torques for an initial response of 50ms averaged
over all 19 trials of the two subjects. Results are shown for the model with and without SRS and for a standard
and a high tendon stiffness value kT. Right: Muscle and ID (black) torques for a forward trial of S1.
Models of Trial-to-Trial Error Correction Dynamics for a 2D Redundant Reaching Task
1
Mary Rose Devine ([email protected]) and 1Jonathan Dingwell
1
University of Texas, Austin, TX, USA
Modifying and correcting movements based on the error of a previous movement is a fundamental task in neuromuscular control. Linear feedback system (LDS) models can provide clear mathematical representations of
such processes. In this study, two LDS models [2,4] for trial-to-trial control were compared to previously published experimental data for a redundant reaching task [4]. We compared one model developed to predict error
correction for tasks with explicitly redundant goals [3,4] to a model developed to understand how the structure
of motor noise affects control [1,2]. Recently, the model of [1] was applied to a redundant reaching task by considering the redundant direction as perfectly uncontrolled [2], instead of weakly controlled, as in [3]. Here, we
compared the ability of each model to explain the observed serial correlation structure in the experimental data.
The following adapted general form contains all of the components of both model structures:
0
(1)
√1
√1
√
0
The vector variable xn represents the coordinates in goal space, at discrete time n, where the first dimension is
tangent (“irrelevant”, δT) to the redundant goal and the second dimension is perpendicular to it (“relevant”, δP).
The µ terms are the correction rate parameters, and w is the fraction of noise attributable to hidden state level
noise, as only in [1,2]. The model in [1] has both control parameters µT and µP free, but only one noise term, such
that (1-w)=0. The model in [2] considers µT=0, 0<w<1, and µP a free parameter. For each model, the control
parameter(s) were incremented evenly from 0 to 1, and the lag-1 autocorrelation of the resulting simulated time
series were averaged via Monte Carlo simulation methods. In the experimental reaching data, ten participants
made 400 consecutive reaches and were instructed to minimize errors provided by visual feedback only [4].
Figure 1: Simulated autocorrelation ranges for positive correction rates. Experimental results are duplicated in all plots for
comparison. A: Model as
in [3]. B: Model as in [2].
C: Model as in [2] plus the
hidden noise of [1,3].
If the tangent direction is assumed uncontrolled, as in [2], the model is only able to replicate some of the observed
tangential lag-1 autocorrelation values by varying the noise parameter w (Fig. 1A). Conversely, the model of [3]
was able to replicate any pair of autocorrelations (Fig. 1B). In further simulations, the noise structure of [3] was
modified to include the two sources that are the defining feature of [1,2]; Fig. 3C shows that any lag-1 autocorrelation could then be replicated for a hidden state noise structure, but only by allowing nonzero tangential control.
References
van Beers, RJ (2009) Motor learning is optimally tuned to properties of motor noise. Neuron 63: 406-471
van Beers, RJ, Brenner, E, and Smeets, JBJ (2013) Random walk of motor planning in task irrelevant directions. J. Neurophysiol. 109: 909-977.
3. Dingwell JB, John J, & Cusumano JP (2010) Do humans optimally exploit redundancy to control step variability in walking? PLoS Comp. Biol. 6: e10000856.
4. Dingwell, JB, Smallwood, RF, & Cusumano, JP (2013) Trial-to-trial dynamics and learning in a generalized,
redundant reaching task. J. Neurophysiol. 109: 225-237.
1.
2.
1,2,3
SICI During Voluntary Movement Reveals Persistent Impairment in Cortical Stroke
Qian Ding ([email protected]), 1,2,3Sahana M. Kamath, 2,3William J. Triggs, and 1,2,3Carolynn Patten
1
Neural Control of Movement Lab
2
Malcom Randall VAMC and 3University of Florida, Gainesville, FL, USA
Short intracortical inhibition (SICI) is a GABAA-mediated phenomenon argued to mediate motor selectivity.
Previous work reports reduced SICI, corresponding with motor disinhibition, in the sub-acute period following
cortical (CORT), but not subcortical (SC), stroke [1] which may normalize as part of the natural course of
stroke recovery [2]. Importantly, SICI is typically measured at rest complicating our understanding of its role in
motor control and recovery following stroke. Here we investigated task-dependent differences in SICI
hypothesizing: i) SICI measured at rest (SICIrest) and during voluntary movement (SICIactive) would differ and ii)
SICIactive would reveal persistent impairments following CORT stroke.
We tested 15 adults (63±9.4 yr, 13 male) with chronic (78.9±51.4 mo) stroke (7 CORT, 8 SC) and 9 controls
(CON)(59.7±7.1 yr, 5 male) using paired-pulse transcranial magnetic stimulation during three tasks: rest, grip,
box & blocks (B&B). Motor evoked responses (MEPs) were measured from the first dorsal interosseous of the
paretic and non-dominant hands of Stroke and CON, respectively. SICI was induced by conditioning the test
MEP at 0.8 resting/0.7 active motor threshold at the interstimulus interval producing maximal SICI at rest
(3.1±1.1 ms CON, 3.5±1.2 ms Stroke). Stimulation intensity was adjusted across tasks to maintain test MEP
amplitude at 1mV pk-pk. SICI was quantified as the ratio of conditioned/unconditioned MEParea.
The magnitude of SICIrest was similar across CON, CORT, and SC (p>0.5) and not correlated with clinical or
performance measures including: grip strength, B&B, or UE Fugl-Meyer Assessment (p’s >.05). However,
SICIactive, specifically during B&B, was significantly reduced in CORT compared to rest (p’s <.008).
Figure 1.
"
"
A. SICIactive, measured
during B&B, correlates with
B&B score similarly across
CON, CORT, and SC (slope,
p’s >.05)
"
"
B. B&B scores span a
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similar range in CORT and
SC, but reveal a higher
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intercept in CORT (p = .03).
SICIactive is related to motor performance and reveals important group differences not detected by SICIrest.
Dysregulation of GABAA circuits is more profound in CORT, and appears to result from stroke location rather
than magnitude of motor impairment. The presence of persistent impairment argues against a role of reduced
SICI in support of neural plasticity and recovery early post-stroke [1]. Instead, disinhibition of the ipsilesional
hemisphere during voluntary movement may interfere with intended results of current rehabilitation practices.
References
1. Shimizu T. (2002). Motor cortical disinhibition unaffected hemisphere. Brain, 125(8): 1896–1907.
2. Huynh, K.V. (2013) Longitudinal plasticity across the neural axis acute stroke. NNR, 2013: 27(3): 219-29.
Acknowledgments
Supported by the Department of Veterans Affairs, Rehabilitation RR&D Service (Research Career Scientist
Award 3F7823S, Patten, PI), University of Florida Graduate School Fellowship (Ding, Kamath).
How Humans Regulate Lateral Stepping Movements and Balance While Walking
Jonathan B. Dingwell ([email protected]), 2Joseph P. Cusumano ([email protected]), 1,3Jonathan H.
Rylander ([email protected]), 1Jason M. Wilken ([email protected])
1
University of Texas at Austin, Austin, TX, USA, 2 Pennsylvania State University, University Park, PA, USA
3
Baylor University, Waco, TX, USA, 4 Brooke Army Medical Center, Ft. Sam Houston, TX, USA
1
Walking humans are inherently more unstable laterally [1,2]. However, it is not clear how humans regulate their
steps to achieve lateral stability. Because of neuro-biomechanical redundancy, there are an infinite number of
potential strategies one could adopt [3]. Identifying which strategy(ies) people use is therefore critical.
Here, we tested 3 candidate strategies that best captured the key relevant features of step-to-step dynamics. These
included (Fig. 1): maintain absolute lateral position (zB), maintain forward heading (i.e., keep walking in the +x
direction: 'zB), or maintain constant step width (w). For each, we derived the corresponding stochastically optimal
control law [3] and simulated stepping dynamics for varying control gains (20 trials of 1,000 steps each). We
compared theoretical predictions to experimental data from 13 healthy subjects (age 22-40). For each time series,
we computed means, standard deviations, and Detrended Fluctuation Analysis (DFA) D exponents quantify the
degree to which deviations were either corrected or allowed to persist from each step to the next [3].
Stepping movements for both humans and models reflected the redundancies exploited by each (Fig. 1). Humans
exhibited stepping dynamics most consistent with step width control, but controlling step width alone did not
capture all of the dynamics exhibited by humans. This suggests humans walk with a hierarchical / multi-objective
strategy that prioritizes step width, but also regulates lateral position and/or heading to lesser degrees. Prioritizing
step width control is likely directly related to maintaining lateral balance/stability [1,2] and is thus highly relevant
for those prone to falling. Additional experimental results (not shown) in both non-impaired participants and
individuals with unilateral amputation tested during both unperturbed and laterally perturbed walking confirm
that humans adopt multi-objective stepping control strategies that are highly adaptable to changing contexts.
DFA Exponent (α)
Position (zB)
w = zR - zL
HUM
zB
ΔzB
w
1.5
zL
ΔzB
Heading (ΔzB) Step Width (w)
1.0
zR
0.5
zB
0.0
x
z
0.6 0.8 1.0 1.2 1.4
0.6 0.8 1.0 1.2 1.4
Controller Gains
0.6 0.8 1.0 1.2 1.4
Figure 1: Left: Candidate variables
for control: absolute position (zB),
“heading” ('zB), or step width (w).
Right: DFA D for step-to-step fluctuations in each stepping variable
[D(zB), D('zB), D(w)] for each control model. Horizontal lines show
ranges (mean±SD) for Humans.
References
1. Kuo AD (1999) Stabilization of lateral motion in passive dynamic walking. Int J Robot Res 18:917–930.
2. McAndrew PM, Wilken JM, and Dingwell JB (2011) Dynamic stability of human walking in visually and
mechanically destabilizing environments. J Biomech 44:644–649.
3. Cusumano JP, Mahoney JM, and Dingwell JB. (2014) The dynamical analysis of inter-trial fluctuations near
goal-equivalent manifolds. Adv. Exp. Med. 826: 125–145.
Acknowledgments
Funding provided by NIH #HD059844 (JBD & JMW) and DOD #W81XWH-11-2-0222 (JBD, JPC, & JMW).
Disclaimer
The views expressed herein are those of the authors and do not reflect the official policy or position of Brooke Army Medical Center, U.S. Army
Medical Department, U.S. Army Office of the Surgeon General, Department of the Army, Department of Defense or the U.S. Government.
The Real-World Challenge Point Hypothesis:
Predicting the consequences of challenge for unsupervised motor training
1
Jaime E. Duarte ([email protected]) and 2David J. Reinkensmeyer
1
ETH Zurich, Zurich, Switzerland, 2 University of California, Irvine, CA, USA
A key issue in the design of interventions for motor learning and neurorehabilitation is providing the
appropriate challenge level during training. The Challenge Point Framework of Gudagnoli and Lee [1] uses the
challenge level of a task to optimize the relation between the performance during practice and the potential for
motor learning (Fig 1, left). Another factor that often plays a key role in motor learning and neurorehabilitation
is how the challenge level affects the motivation of the trainee to engage in training. That is, a task must not
only optimally challenge the motor abilities of the trainee, but it must also promote practice beyond the training
session. We hypothesize that these two goals are often odds, resulting in an altered, optimal challenge point.
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To study how the challenge level affects motor learning and motivation we used robotic devices to regulate the
challenge level of a task by modulating the performance errors during training—e.g. using haptic guidance/error
augmentation—or changing the effort required to perform a task—e.g. increasing the force required to move an
object. Here we briefly discuss these interventions and summarize how their results led us to expand the
Challenge Point Framework to include the trainee’s
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Figure 1. The Real-World Challenge Point framework.
Accounting for the willingness to practice may lead to new
optimal challenge levels that maximize motivation.
In the first study, we temporarily increased the kinematic
errors of unimpaired participants performing a virtual
golf-putting task. This form of training did not improve
motor learning when compared to regular training, but
did decrease self-reported motivation to participate in
training even days after the intervention [2]. In the second
study, we changed the force required to perform a pulling
task for rats recovering from a cervical spinal cord injury.
The rats that trained with higher forces performed the
pulling task less frequently, but showed greater recovery
in strength when compared to rats that trained with lower
forces and pulled more frequently. That is, lower dose,
measured in terms of repetitions, led to greater recovery,
when challenge was greater.
By considering the simultaneous effects of challenge on motor learning and motivation, we predict a shift in the
optimal challenge level predicted by the Challenge Point Framework. We refer to this shifted challenge level—
which accounts for the trainee’s willingness to engage in the task—as the real-world challenge point (Fig 1,
right). We use the term real-world because this point describes the behavior of the trainee during unsupervised
motor training, such as training at home, where training amounts are left up to the trainee. Within this
framework we propose to use line-search algorithms to efficiently identify the real-world challenge point based
on experimental data, as we will demonstrate in this poster. This work thus provides a principled approach to
the design of robotic training algorithms for at-home and other types of unsupervised, “real-world” training.
References
Guadagnoli MA and Lee TD (2004) Challenge point: a framework for conceptualizing the effects of various
practice conditions in motor learning. J Mot Behav 36:212–24.
2. Duarte JE and Reinkensmeyer DJ (2015) Effects of robotically modulating kinematic variability on motor
skill learning and motivation. J Neurophysiol 113:2682–91.
1.
Postural Complexity Predicts Increased Postural Sway Following Removal of Visual Sensory Cues
1
Peter C. Fino ([email protected]), 1Martina Mancini, 1Clayton Swanson, 1Fay Horak, 1Laurie King
1
Oregon Health and Science University, Portland, OR, USA
Postural complexity has increasingly been used as an indicator of motor control adaptability where higher
complexity represents more robust, automatic control [1]. Decreased single-task postural complexity has been
associated with instability when cognitive dual-tasks are present [1]. Yet, it is unclear whether this predictive
capacity of postural complexity applies more broadly to other challenges, such as maintaining balance under
varying sensory conditions. We hypothesized that postural complexity during eyes open, quiet stance would
predict balance performance when sensory conditions were altered.
Eighty-six young adults (45M / 41F), mean (SD) 20.7 (1.5) years of age, 176 (9) cm tall, 76.7 (20.4) kg, gave
informed written consent to participate. Participants performed four 30 second standing trials: eyes open on firm
ground (EO-Firm), eyes closed on firm ground (EC-Firm), EO on foam (EO-Foam), and EC on foam (EC-Foam).
Accelerations of the L5-S1 were collected using an Opal sensor (APDM, Portland, OR, USA). Traditional
measures of postural sway such as 95% ellipsoidal area, sway velocity, path length, and RMS were calculated in
both anteroposterior (AP) and mediolateral (ML) directions. In addition, raw signals were high-pass filtered >1
Hz and the multi-scale composite fuzzy entropy was computed over the first 30 timescales, corresponding to
frequencies between 1.4 – 42.7 Hz, in both AP and ML directions. The sum over scales 1-30 defined the
complexity. The visual (EC-Firm/EO-Firm), proprioceptive (EO-Foam/EO-Firm), and vestibular (EC-Foam/EOFirm) ratio scores were calculated for each outcome. Correlations between the EO-Firm complexity and the ratio
scores of the standard metrics were compared using Pearson correlation coefficients in MATLAB using a 0.05
significance level.
Significant negative correlations were found between AP complexity
and visual ratio score for AP path length (ρ = -0.24, P = 0.02), and
between ML complexity and the visual ratio score for ML path length
(ρ = -0.24, P = 0.02, Figure 1). No significant correlations were found
between complexity and any vestibular or proprioceptive ratio scores.
Our results suggest that complexity of postural sway predicts
adaptability to visual cues. However, complexity’s association with
sensory adaptation was confined to vision, likely due to vision’s
influence on the spectral density of sway throughout the frequency band
Figure 1: ML path length visual ratio from 1-10 Hz [2]. These results bring some clarity to the interpretation
of postural complexity which has increasingly been used to identify
score vs. ML EO-Firm complexity.
populations with balance disorders. This correlation to future
Linear fit shown in red.
performance suggests that decreased complexity represents not only
differences in attention towards balance control [1], but also differences in the sensorimotor integration of visual
information. These results suggest complexity may reflect the overall adaptability of the postural control system.
References
1. Manor B, et al. (2010) Physiological complexity and system adaptability: evidence from postural control
dynamics of older adults. J Appl Physiol 109:1786–1791.
2. Singh NB, et al. (2012) The spectral content of postural sway during quiet stance: Influence of age, vision,
and somatosensory inputs. J Electromyogr Kinesiol 22:131–136.
Acknowledgments
Supported by NIH R21HD080398, OCTRI KL2TR000152, UL1TR000128
1
2
Design and evaluation of a novel mechanical device to improve hemiparetic gait: a case report
Krista Fjeld1 ([email protected]), Siyao Hu2, Katherine J. Kuchenbecker2, Erin V. Vasudevan1
Division of Health and Rehabilitation Sciences, School of Health Technology and Management, SUNY Stony
Brook University, Stony Brook, NY, USA
Department of Mechanical Engineering and Applied Mechanics, School of Engineering and Applied Science,
University of Pennsylvania, Philadelphia, PA, USA
OBJECTIVE: Hemiparetic or asymmetric gait is a frequent and disabling consequence of unilateral brain injury
and stroke. Although hemiparetic gait is thought to stem from insufficient propulsive force generated by the
paretic leg [1-3], few interventions have targeted paretic leg propulsion for improvement. Our objective is to
test the hypothesis that walking with a simple mechanical device that periodically resists forward movement of
the body will increase propulsive force generation by the paretic leg.
METHOD: We designed a low-cost Gait Propulsion Trainer (GPT) that includes a
cable spool attached to a stand at waist level. The end of the cable attaches to a
belt worn around the hips. As the person walks over ground away from the spool,
a rotary brake periodically resists the spool’s rotation, which increases the cable
tension and resists forward movement of the person’s trunk. Brake activation is
controlled by pressure sensors that are taped to the shoe soles. The pressure
sensors turn the brake on during paretic leg stance phase and off during nonparetic leg stance. The experiment consisted of ten baseline walking trials over a
10 m walkway, ten GPT trials, and ten post-GPT trials (i.e., normal walking). A
24-year-old female with left-side hemiparesis following brain injury was tested.
RESULTS: GPT resistance increased paretic leg propulsive forces generated in
late stance by 25% over baseline values. Importantly, increased paretic propulsion Figure 1: GPT design
persisted when GPT resistance was removed in post-braking trials, even though
the participant walked with the GPT for only a short training period (ten 10 m trials).
CONCLUSIONS: We found that a person with hemiparetic gait increased paretic leg propulsion during and
after GPT training. Since the GPT training period was so short, increases in paretic leg propulsion forces were
probably due to a recalibration of neuromotor commands during walking, resulting in greater recruitment of
paretic limb extensor muscles. Longer-term training may augment this effect by strengthening paretic limb
extensors and improving their force generating capacity. By developing a novel, targeted and low-cost approach
to gait training, we aim to create an effective and accessible physical therapy option for the millions of people
with hemiparesis who want improve their walking.
References
1 Nadeau, S., Gravel, D., Arsenault, A.B., and Bourbonnais, D.: ‘Plantarflexor weakness as a limiting factor
of gait speed in stroke subjects and the compensating role of hip flexors’, Clin Biomech (Bristol, Avon),
1999, 14, (2), pp. 125-135
2 Hsiao, H., Awad, L.N., Palmer, J.A., Higginson, J.S., and Binder-Macleod, S.A.: ‘Contribution of Paretic
and Nonparetic Limb Peak Propulsive Forces to Changes in Walking Speed in Individuals Poststroke’,
Neurorehabil Neural Repair, 2015
3 Routson, R.L., Clark, D.J., Bowden, M.G., Kautz, S.A., and Neptune, R.R.: ‘The influence of locomotor
rehabilitation on module quality and post-stroke hemiparetic walking performance’, Gait Posture, 2013, 38,
(3), pp. 511-517
Visuomotor Entrainment and the Control of Balance in Walking
Jason R. Franz ([email protected]), 2Carrie A. Francis, 2Matthew S. Allen, and 2Darryl G. Thelen
1
University of North Carolina and North Carolina State University, Chapel Hill, NC, USA
2
1
In standing balance control, vision is actively used to minimize errors between the perception of motion and actual
motion of the head and trunk [1]. Similarly, we find evidence in walking that subjects may synchronize their
kinematics to frequencies present during visual perturbations, a behavior we refer to as visuomotor entrainment.
This study investigated the prevalence of visuomotor entrainment and its relevance to walking balance control.
Ten young adults walked in a virtual reality environment which perturbed visual flow with systematic changes in
the driving frequencies of perceived mediolateral (ML) motion (Fig. 1). First, we hypothesized that ML motion
in walking exhibits naturally emerging entrainment to a broad range of driving frequencies. Second, we
hypothesized that effects on walking balance would be a function of proximity to the temporal resolution of foot
placement control. Here, balance would be better preserved when perturbations allowed sufficient time for
corrective actions (i.e., driving frequency << stride frequency) or were mitigated via low-pass neuromechanical
filtering (i.e., driving frequency > stride frequency). Spectral analysis quantified the intensity of perturbation
frequencies in ML postural sway. We quantified dynamic balance using the standard deviation and temporal
persistence (via detrended fluctuation analysis) of step width, step length, and postural sway.
The spectrum of mediolateral motion revealed naturally emerging entrainment across a broad range of frequencies
of perceived ML motion (Fig. 1B). Effects on balance control were frequency-dependent, with greatest variability
observed for the perturbation frequency nearest subjects’ stride frequency (i.e., Pair 4). For all but Pair 4, visual
perturbations progressively decorrelated step width with increasing perturbation frequency. These changes were
accompanied by stronger temporal anti-persistence of postural sway. Thus, for most conditions, visual
perturbations infiltrated the planning and execution of foot placement as subjects sought to rapidly correct postural
disturbances. In contrast, Pair 4 alone strengthened step width temporal persistence and decorrelated postural
sway. With its close proximity to stride frequency, Pair 4 allowed insufficient time to plan and execute foot
placement adjustments in response to postural disturbances. This behavior also explains the simultaneous
decorrelation of postural sway, implying that postural disturbances were not well corrected from step to step.
Visuomotor entrainment is a robust and naturally emerging phenomenon in walking, involving adjustments in
postural control at frequencies directly present in available visual information. Walking balance exhibits a
complex, frequency-dependent response to visual information; foot placement and postural sway were especially
disrupted when perturbations included information at frequencies nearest subjects’ stride frequency. These
insights may facilitate diagnostic or rehabilitative approaches for sensory-induced balance impairments.
Figure 1. (A) Subjects in our virtual environment exhibited (B) naturally emerging entrainment to visual perturbations.
Black lines indicate normal walking. Dashed lines indicate perturbation frequencies. *different from unperturbed (p<0.05).
References
1. Dijkstra TMH et al. (1994). Biol Cyber 71: 489-501.
Acknowledgments: Gratefully, Dr. Shawn O’Connor for his assistance with our virtual environment.
Atrophy and Fatty Infiltration at the Paretic Elbow in Individuals with Chronic Hemiparetic Stroke:
Preliminary Findings
1
L. Garmirian ([email protected]), 1R Schmid, 1M. Wasielewski, 1A. M. Acosta, 1T.
Parish and 1J. P. Dewald
1
Northwestern University, Chicago, IL, US
Background and Aim: The long-term effects of motor impairments on upper limb muscle architecture are
unknown post stroke. It is hypothesized that motor impairments may cause decreased neural activation and
subsequent decreased use of the paretic upper limb, which over time may cause muscle atrophy and fatty
infiltration. The aim of this research is to quantify long-term changes in muscle volume and intramuscular fat
following hemiparetic stroke in the paretic elbow.
Methods: Magnetic resonance images were acquired from 4 stroke subjects, 2 males and 2 females with an
average age of 61, using a 3D gradient echo pulse sequence of the upper limb (TR=7ms, flip angle=12°, matrix
size = 256x216, slice thickness = 3mm). The Dixon method [1] was used to estimate percent fat using an
echotime (TE) of 2.39ms, when water and fat are in phase and a TE of 4.77ms, when water and fat are out of
phase. Using AnalyzeDirect, manual segmentation of the biceps, triceps and brachialis was done to measure
volume and percent intramuscular fat. Percent fat was calculated using a ratio of the intensity of the fat image
compared to the intensity of the water image.
Results: For biceps, the percent difference in contractile element volume was 37% due to a 35.7% difference in
total muscle volume and 1.675% greater intramuscular fat in the paretic biceps compared to the non-paretic
biceps. For triceps, the percent difference in contractile element volume was 34.2% due to a 33.6% difference in
total muscle volume and 0.89% greater intramuscular fat in the paretic triceps compared to the non-paretic
triceps. For brachialis, the percent difference in contractile element volume was 18.1% due to a 24.3%
difference in total muscle volume and 2.34% greater intramuscular fat in the paretic brachialis compared to the
non-paretic brachialis.
Conclusions: The volume of contractile element in the paretic elbow was less compared to the non-paretic
elbow for these three elbow muscles. This was in large part due to a decrease in total muscle volume in the
paretic elbow and to a much lesser extent to an increase in intramuscular fat. The study of such changes at more
distal muscles is still underway.
Significance: Deficits post stroke, especially musculoskeletal changes like muscle fat infiltration and atrophy,
are not fully understood. Additionally, rehabilitation of the upper limb post stroke varies widely and outcomes
are variable. Further information about musculoskeletal changes post stroke may help guide rehabilitation
towards more efficacious treatments aimed at decreasing the rate of atrophy and fatty infiltration. An example
of an approach that may be effective is using electromyography (EMG) driven functional electrical stimulation.
The methodology developed in this study can also be used as a sensitive measure to track the efficacy of these
interventions.
References
1. Gaeta M. et al (2001) Muscle Fat Fraction in Neuromuscular Disorders: Dual-Echo Dual-Flip-Angle Spoiled
Gradient-Recalled MR Imaging Technique for Quantification--a Feasibility Study. Radiology 259:487-94.
Acknowledgments
Supported by NIH R01HD084009-01A1
Isolating Sensory Pathways for Interlimb Modulation of Human Locomotor Output
12
Tracy N. Giest ([email protected]) and 1Young-Hui Chang
1
Georgia Institute of Technology, Atlanta, GA, USA
2
Interlimb coordination is paramount to dynamic stability during locomotion [1]. Presently, there is a lack of
fundamental knowledge on how afferent feedback modulates the interlimb neural control during human
locomotion. Our work addresses a fundamental role of afferent feedback in regulating interlimb coordination
during human locomotion. The purpose of this work is to present a novel, non-invasive paradigm for perturbing
afferent feedback during human locomotion in the absence of mechanical interlimb coupling, and to quantify
the subsequent effects of a reversible below-knee ischemic deafferentation (ID) on contralateral limb motor
output. Previous work in cycling reported a locomotor-dependent reduction in contralateral flexor motor
activation during high rhythmic extension on the ipsilateral side [2]. We hypothesized that a decrease in
ipsilateral plantarflexor afferent feedback (due to ID) would cause an increase in contralateral flexor muscle
(tibialis anterior and rectus femoris) motor output.
We used a custom-built cycle ergometer with mechanically decoupled cranks to perturb right leg afferent
feedback without altering left leg task mechanics. This is an ideal paradigm to probe locomotor interlimb
coordination, as it prevents confounders that would occur during walking with ID (i.e altered balance or stanceswing times). Eight able-bodied subjects, trained on the mechanically decoupled cycle ergometer, successfully
completed the Georgia Tech IRB approved protocol (7 males; age: 29.69 ± 5.21 years; mass: 82.60 ± 6.77 kg).
Subjects first completed a 45-second bilateral pedaling trial that served as a baseline. A blood pressure cuff was
then applied below the right knee and inflated to 220mmHg to achieve ID. Sensory loss was verified with
Semmes-Weinstein filaments. Subsequent pedaling trials were collected at 0, 4, 12, and 20 minutes postapplication of the blood pressure cuff. A cadence of 60 rpm was maintained for all conditions, and kinematics
(120Hz, Vicon), kinetics (300Hz, Kistler), and electromyographic data (1080Hz, Noraxon) were collected.
Contrary to our hypothesis, we observed a reduction in the motor output of the left tibialis anterior and rectus
femoris (Figure 1A&B). Left leg kinematics had no significant difference from baseline. Thus, we propose an
interlimb pathway whereby below-knee extensors facilitate activation of contralateral flexors (Fig. 1C, green,1).
Additionally, our findings suggest a crossed limb inhibitory pathway between above-knee extensors and
contralateral flexors that may further explain the previous findings of Ting et al. (Fig. 1C, orange,2).]
Mean Normalized EMG
A.
Tibialis Anterior
Q1
1.5
Q2
Q3
Rectus Femoris
Q4
Q1
*
1
Q2
Q3
Q4
Q1
*
1
C. Proposed eﬀect of right leg below-knee IDD
0.5
0.5
Le Leg
0
0
0
45
B.80
90
135
180
225
Crank Angle
270
315
Q1: iEMG Tibialis Anterior
*
60
iEMG
Q1
*
360
0
*
20
20
0
4
12
20
Minutes of Ischemic Deafferentation
References
1. Stevenson
0
135
180
225
Crank Angle
270
315
360
*
Baseline
0
*
*
Right Leg
AK
Ext
BK
2
1
Q1: iEMG Rectus Femoris
60
40
Baseline
90
80
40
0
45
AK
Ext
BK
*
4
12
20
Minutes of Ischemic Deafferentation
Flex
Flex
Figure 1: A. Mean left leg TA and
RF EMG traces for all conditions.
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coordination circuitry0) !
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-
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AJT, et al. (2015) Interlimb communication following unexpected changes in treadmill velocity
during human walking. J Neurophysiol113: 3151–3158, 2015.
2. Ting LH, et al. (2000) Contralateral movement and extensor force generation alter extension phase muscle
coordination in pedaling. J Neurophysiol 83:3351-3365.
Acknowledgments: Supported by NICHD 5T32HD055180 to TG and NINDS 5R01NS069655 to YHC.
1,2
Learning Walking Stability
Keith E. Gordon ([email protected]), 1Mengnan (Mary) Wu and 1Geoffery Brown
1
2
Edward Hines Jr. VA Hospital, Hines, IL, USA
Since BANCOM 1996, the prospect of recovering locomotion following incomplete spinal cord injury (iSCI) has
changed from the exception to the expectation. Despite the success of interventions leveraging the plasticity of
spinal networks to restore rhythmic stepping, deficits in locomotor stability persist. In the next 20 years we must
develop and translate a neuromechanical framework to address walking stability. Our purpose is to understand
how individuals with iSCI learn gait stability. Specifically, we induced locomotor instability by having subjects
walk briefly with external stabilization that reduced requirements to actively control frontal plane center-of-mass
motion. When the stabilizing field is removed, we have previously demonstrated that people exhibit a predictable
after-effect of decreased lateral stability that parallels a temporary reduction in step width. This unique induced
destabilization disrupts subjects’ internal model creating a challenge to the nervous system’s ability to control
self-generate movements. We hypothesized that with repeated exposure the initial step width reduction during the
after-effects period would be diminished suggesting that individuals with iSCI can improve their ability to form
and update an appropriate internal model for gait stability.
Seven ambulatory subjects with iSCI performed four treadmill walking trials of 400 steps. The first 100 steps
established a Baseline measure of walking with no external assistance. The next 200 steps were performed in
either a Null field or a Stabilizing viscous lateral force field. Finally, any applied forces were removed and
subjects walked for another 100 steps to measure After-Effects. The Stabilizing condition was repeated 3 times.
Stabilizing forces were applied to the pelvis via motorized cables. These applied forces were proportional in
magnitude and opposite in direction to the subject's lateral center-of-mass velocity. The viscous field reduced the
requirements to actively maintain straight-ahead walking.
With practice, gait instability during the after-effects period was noticeably reduced. Specifically, the initial
decrease in step width during the after-effects period (difference each trial between after-effects step 1 and
baseline step 100) was significantly greater during the first two Stabilizing fields than the Null field (p < 0.047)
(Fig 1). However, by the third Stabilizing field exposure, the initial after-effects decrease in step width was not
significantly different than the Null field (p = 0.467). EMG data suggest that subjects used variable strategies to
increase lower-limb impedance at selective joints and planes of motion.
Figure 1: Difference in step width
These results suggest that individuals
between Baseline and the After-Effects
with iSCI have the neural resources to
period of each trial. Compared to the
learn gait stability. Specifically, our
Null Field, step width decreased
findings indicate that individuals with
significantly after the first two exposures
iSCI can learn to rapidly adjust their
to the stabilizing field. By the third
neural control strategy to maintain
exposure to the stabilizing field, changes
stability in situations when their
in step width were not significantly
internal model is not appropriate.
different than the Null Field.
However, while the resulting
kinematic behaviors displayed with practice were similar, the underlying neural control strategies used to create
stable gait were highly variable. These differences may provide insight about what strategies are more effective
and efficient for managing challenges to stability.
Acknowledgments
Supported by Career Development Award #1 IK2 RX000717-01 from the United States Department of
Veterans Affairs, Rehabilitation Research.
‘Point’-blank distinction in lower limb walking coordination following stroke
Kreg Gruben ([email protected]), Wendy Boehm
Walking is a complex dynamic task that humans typically execute with ease. Walking with hemiparesis following
stroke, however, evokes a tremendous challenge to that task. Extensive evidence indicates that disrupted lower
limb muscle coordination contributes to that challenge, but an exact characterization of the miscoordination, how
behaviors are disrupted, and therapy to restore walking have not been realized. To better characterize this
impairment, this study analyzed the ground reaction force (F) during walking in impaired individuals for
comparison with non-paretic individuals. The result was a distinct difference in the lower limb muscle
coordination pattern between paretic (P) and non-paretic (NP) limbs that predicts walking difficulties, behavioral
compensations, and therapy objectives.
Previous study of sagittal-plane F in non-disabled human walking has shown linear relationships between center
of pressure (CP) and tangent of F direction off vertical (tan(șF)).1 During single-leg stance, that CP vs tan(șF)
relationship is geometrically represented as F lines-of-action being directed through a fixed intersection point
above the center of mass (CM) called a divergent point (DP). When the similar relation is extracted using CP with
the effect of heel-to-toe foot roll removed, the F lines-of-action intersect lower (xi), near the CM.2
A line captured most of the CP vs tan(șF) variance in DP/xi
(variance accounted for: control walk 99%/95.6%, control CSV
walk 99%/97%, stroke CSV walk 93%/97.9%). The DP and xi
locations for typical and CSV walking was above and near the CM
(just above the hip), respectively, for the control participants and the
NP limb of stroke participants (Fig. 1). In the P limb, DP location
was more variable (Fig. 1) and the P xi was 0.09m anterior of the
NP xi on average.
height (fraction of hip height)
Six control (4 female, age 20í53yrs) and 3 chronic post-stroke (2 female, age 57í78yrs, 1 right-sided P)
participants walked on a custom force treadmill with programmable motion 6-axis foot plates under each foot.1
All participants walked with a simplified constant-swing-velocity
(CSV) foot motion pattern. Control participants additionally walked
4
with a typical swing velocity. F was recorded at 100Hz for 15s. The
principal component of the CP vs tan(șF) relationship determined
the location of the DP and xi, which was expressed as fraction of hip
3
height.
2
1
0
L
-1
R
non-disabled
L
R
non-disabled
NP
P
stroke
The tight, anteriorly biased xi of the P limb captures a specific shift
walking
CSV walking CSV walking
in coordination that is consistent with previously observed
anteriorly biased F in seated tasks.3 That misdirected F predicts a Figure 1: Height of intersections points
range of behaviors to avoid using this control for support, as is for DP (left end of lines) & xi (right end
commonly observed after stroke.4 The variable DP shows a change of lines) show DP above the CM and xi
in strategy to accommodate this errant control such that a righting near the CM. One line per person per
leg.
torque is still provided by F when CP shifts due to body tip.
References
1. Gruben, K. G., & Boehm, W. L. (2012). 31(3), 649-659.
3. Rogers, L. M., Brown, D. A., & Gruben, K. G. (2004). Gait & Posture, 19(1), 58-68.
4. Boehm, W. L., & Gruben, K. G. (2016). Trans Stroke Res 7(1), 3-11.
Acknowledgments
Replacing the Musculoskeletal Dynamics of the Human Arm by Means of Trickery
Christopher J. Hasson ([email protected]); Northeastern University, Boston, MA, USA
Understanding how the nervous system adapts to modifications of the physical properties of the body is important
for rehabilitation. Several studies have hypothesized that humans create internal representations of their body
dynamics, which are modified with learning. However, since most experiments use dynamics perturbations that
are relatively alien, such as a velocity-dependent “curl” force field, it is unclear whether humans would behave
similarly if their native body dynamics, including intrinsic musculotendon dynamics, were modified.
Tackling this question by directly modifying human tissue raises ethical concerns. An alternative is to trick the
nervous system into thinking the body’s own dynamics have been modified. Such a trick could be accomplished if
an individual’s neural commands were intercepted and routed through a musculoskeletal model with modifiable
dynamics, and sensory feedback was provided to make it seem like the model was in fact their own body.
Myoelectric virtual arms may permit this trick. Historically, these “arms” have been used to simulate human
movement [1], but recent work explored their use in a motor adaptation context [2-4]. Sensory feedback is usually
limited to a visual display, which may not be enough to convince individuals that a virtual arm is their arm ௅ but
providing additional proprioceptive feedback might do the “trick”. This can be achieved using a motor to move the
person’s limb to match the virtual arm motion, an approach used previously in force-based motion control [5].
This study takes the first step towards performing the trick of replacing the musculoskeletal dynamics of a human
arm by developing a personalized myoelectric virtual arm with visual and servomotor-induced proprioceptive
feedback. After subjects practiced a goal-directed task with their real and virtual arms, the similarity between the
performance and control of these two arms was compared; for the trick to be successful high similarity is needed.
Three subjects participated to date. The inertial properties of the virtual arm and strength of the virtual muscles were
customized to each subject. Muscle activity from the biceps and triceps was converted to excitation signals that
activated virtual biceps and triceps muscles, which moved a one degree-of-freedom virtual arm in silico. This motion
was displayed on a monitor (Fig. 1A) and a servomotor augmented the torque produced by subjects’ muscular action
to make the real arm motion match the virtual arm’s, replacing the dynamics of the real arm with the virtual arm.
45o
Waypoint
B
0o
Virtual Arm
θ
Start/Finish
2.2
2.0
1.8
1.6
1.4
1.2
1.0
Actual
Arm
Virtual Arm
Biomechanics
Personalization
A
Movement Time (s)
After two days of practicing a back-andforth slice movement with maximum speed
and accuracy, task performance with the
virtual arm approached the actual arm (Fig.
1B). Early in practice subjects had high
muscular activation and co-contraction, but
by the end, muscular activation patterns
became more natural and subjects reported
that they did not notice the motor.
0.8
Practice
Figure 1. A) Visual feedback of virtual arm. B) Preliminary data from an
exemplar subject performing the task with her real and personalized
myoelectric virtual arm. Data points represent binned data (15 trials each).
The next step to be addressed in future research is to modify the virtual arm dynamics after subjects have adapted.
This method for providing an individual with a temporary “new” arm through a myoelecto-mechanical interface
could be used to gain new insights into how the nervous system adapts to neurological impairments and test novel
methods of reducing the deleterious effects of movement disorders, such as dystonia or dyspraxia.
References
1. Manal K, et al. (2002) A real-time EMG-driven virtual arm. Comp. Biol. and Med. 32:25-36.
2. Hasson CJ. (2014) Neural representation of muscle dynamics in voluntary movement control. Exp. Brain Res. 232(7):2105-2119.
3. Hasson CJ, et al. (2015) Effects of kinematic vibrotactile feedback on learning to control a virtual prosthetic arm. JNER 12(1):31.
4. Hasson CJ, et al. (2016) Neural control adaptation to motor noise manipulation. Front. Hum. Neurosci. 10:59.
5. Kuchenbecker KJ, et al. (2007) Quantifying the value of visual and haptic position feedback during force-based motion control.
EuroHaptics Conference; Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (pp. 561-562).
The Magnitude of Interlimb Coupling Between Lower and Upper Extremities Appears Linked to Side of
Impairment in Chronic Stroke: Preliminary Findings
1
Rachel L. Hawe ([email protected]) and 1Jules P.A. Dewald,
1
Following a stroke, many individuals demonstrate altered interlimb coupling between their lower and upper
extremities, which can impair gait, balance reactions, and functional use of the arm. The underlying neural
mechanisms of altered interlimb coupling are poorly understood. Additionally, it is not currently known if
interlimb coupling is different between individuals with right and left hemiparesis, as it has recently been shown
that individuals with right-sided brain lesions have difficulty controlling limb impedance for stabilization of
steady-state limb positions [1]. The aim of this study was to quantify the effect of lower extremity efforts on
involuntary upper extremity movement, and compare individuals with right and left hemiparesis.
Twelve individuals (7 male/5 female, average age 59.7±6.7 years) with moderate to severe chronic hemiparesis
and 7 age-matched control subjects (4 males/3 females, average age 58.7±2.6 years) participated. Of the
individuals in the stroke group, 6 had left hemiparesis and 6 had right hemiparesis, with no significant
difference in upper and lower extremity Fugl-Meyer Assessment scores. Participants were seated with their leg
placed in a device to measure isometric knee torques. The upper extremity was held by a robotic device with
haptic springs to provide support while allowing upper extremity movement to occur in any direction.
Participants were instructed to relax their upper extremity while performing maximal and submaximal (25, 50,
and 75%) isometric knee flexion and extension torques. Upper extremity kinematics and kinetics were recorded.
All stroke participants demonstrated greater involuntary upper extremity movements compared to control
participants. Individuals with left hemiparesis were found to have greater extents of upper extremity movement
compared to individuals with right hemiparesis (Fig 1). Upper extremity movement was strongly linked to effort
level, with no significant difference across task (flexion vs. extension) or leg (paretic vs. non-paretic).
Maximum Excursion
Our findings are consistent with the theory that the right hemisphere is lateralized for limb stabilization, as
damage to the right hemisphere results in reduced upper limb stability during lower extremity efforts. Currently,
altered interlimb coupling patterns are rarely addressed clinically, and the current trend of high-intensity gait
training may actually exacerbate their occurrence. Based on the results of this study, therapeutic interventions
may be tailored based on the side of impairments, with individuals with left hemiparesis requiring increased
stability training compared to individuals with right hemiparesis.
0.25
L HEMI
0.2
R HEMI
0.15
0.1
Figure 1: Maximum fingertip excursion normalized to arm length for
paretic knee extension (pKE), paretic knee flexion (pKF), non-paretic
knee extension (npKE), and non-paretic knee flexion (npKF).
0.05
0
pKE
pKF
npKE
npKF
References
Mani S et al (2013) Contralesional motor deficits after unilateral stroke reflect hemisphere-specific control
mechanisms. Brain 136:1288–1303.
1.
Acknowledgments
Supported by AHA 15PRE22990027 and NIH R01HD039343. We would like to thank Stuart Traxel and Paul
Krueger for assistance with experimental setup.
1,2
Motor Planning is Prolonged in the Presence of Uncertainty
Rosalind L. Heckman ([email protected]) and 1,2Eric J. Perreault
1
2
Rehabilitation Institute of Chicago, Chicago, IL, USA
The success of the many tasks we plan and execute every day depends on our ability to rapidly and appropriately
deal with disturbances imposed by our environment. Though temporal information is critical for planning, the
timing of a disturbance is often not known. Knowledge of how we handle temporal uncertainty about a disturbance
is important for understanding our ability to respond and the influence of cognitive and motor impairment.
The aim of this study was to determine how uncertainty about when a postural perturbation will occur influences
the planning process and the efficacy of the eventual motor response. Our hypothesis was that uncertainty in the
timing of the postural perturbation cueing movement initiation would 1) increase the time that a motor response
is planned in advance and 2) decrease the effectiveness of the response elicited by an unexpected perturbation.
Data were collected from 12 healthy subjects (8 male, 19-32 years old) with their right arm attached to a rotary
motor used to pre-activate the elbow extensors and apply elbow flexion perturbations. An auditory WARNING
cued subjects to prepare a ballistic elbow extension movement. Participants were instructed to react as fast as
possible to the GO cue, a postural perturbation of 10º/s and 100 ms. Timing between the WARNING and GO was
varied to study three levels of temporal certainty, each on a different day. For Low and Medium certainty, time
between the WARNING and GO was randomly varied between 5-12 s, and and 2.5-3.5 s, respectively. For High
certainty, the time was fixed at 3 s and an analog countdown clock visually cued the GO. Probe perturbations of
100°/s and 100 ms were presented in 20% of the trials to evaluate the state of motor planning and motor response
efficacy. Probe perturbations were applied before the WARNING, to quantify reflex responses in the absence of
a motor plan, and at various times before GO, to assess the time course of motor planning. Reflex responses were
quantified by the average rectified electromyogram recorded from the lateral head of the triceps 75-100 ms after
perturbation onset. Sternocleidomastoid neck muscle activity indicated the presence of a motor plan (SCM+).
Uncertainty about the timing of a postural perturbation affected the time course of motor planning (Fig. 1).
Average reflex responses evolved similar to planning within this period; however, SCM+ only reflex responses
were larger independent of certainty condition or probe time. Reported differences significant at a level of p<0.05.
1
SCM+ Probability
Low
Medium
High
0.75
0.5
0.25
0
WARNING-50
GO-1000
GO-500
Figure 1. Uncertainty in the timing of the
GO prolonged the planning process. In the
Low certainty condition, the plan was fully
prepared 1000 ms prior to the GO and did
not evolve further. In contrast, motor
planning in the Medium and High certainty
conditions continued to evolve within this
period and was delayed in the High certainty
condition with the countdown clock.
GO-150 GO-0
Time of Probe Perturbations (ms)
Uncertainty about if and when a disturbance will occur greatly influences the temporal evolution of motor
planning and the efficacy of the eventual motor response. Postural perturbations can be used to study this process,
and may be a useful tool for assessing how motor planning is affected by various cognitive and motor disorders.
Acknowledgments
Funding support provided by NIH R01 NS05813 and T32 EB009406.
Are Electrocortical Dynamics of Recumbent Stepping Similar to Treadmill Walking?
1
Helen J. Huang ([email protected]) and 2Daniel P. Ferris
1
University of Central Florida, Orlando, FL, USA
2
Rhythmic whole-body movements such recumbent
stepping are often thought to engage neural networks
similar to walking [1]. Recent studies have shown that
electrocortical dynamics are coupled with the gait
cycle during walking [2]. The purpose of this study
was to determine whether rhythmic whole-body
movements have similar electrocortical dynamics
compared to treadmill walking. We hypothesized that
recumbent stepping and walking would have similar
cortical networks and spectral fluctuations would
occur at gait transitions.
We recorded high-density EEG (Biosemi ActiveTwo,
256 channels) as subjects (n = 17, 12 females, 5
males, 21.1 ± 2.3 years old) walked on a treadmill at
1.2 m/s, performed active recumbent stepping, and
performed passive recumbent stepping. We applied
independent component analysis (ICA) to estimate
the source signals for each merged dataset (walking
and active recumbent stepping; active and passive
recumbent stepping). We then used DIPFIT to model
and localize each source as a dipole. Last, we
identified clusters using a k-means algorithm and
computed event related spectral perturbation (ERSP)
plots for each cluster to examine spectral fluctuations.
Figure 1. A) Clusters found. Yellow = midline
frontal premotor and supplementary motor area; Blue
= left sensorimotor cortex; Red = right sensorimotor
cortex; Teal = left parietal cortex; Magenta = right
parietal cortex; Purple = posterior cingulate; Green =
anterior cingulate cortex. B) Left sensorimotor
ERSPs. Green = no significant difference. Red =
synchronization. Blue = desynchronization. LTO =
left toe off; LHS = left heel strike; RTO = right toe
off; RHS = right heel strike; LFE = left leg fully
extended. RFE = right leg fully extended.
We found six clusters for walking and recumbent
stepping and just four clusters for active and passive
recumbent stepping (Fig. 1A). The anterior cingulate
and left sensorimotor cortices were only identified
with walking data. ERSP plots (Fig. 1B) revealed
increased spectral power prior to toe off for walking
and at the limb transition for recumbent stepping.
Decreased spectral power occurred during single support for walking and leg extension for recumbent stepping
Our findings indicate that rhythmic whole-body movements involve fewer brain areas compared to treadmill
walking, and thus engage a portion of the neural network involved during walking. The left sensorimotor cortex
may be specific to balance control or foot placement during walking, which is not part of recumbent stepping.
References
1. Zehr EP et al. (2007) Neural regulation of rhythmic arm and leg movement is conserved across human
locomotor tasks. J Phys. 582(Pt 1):209-27.
2. Cevallos CD et al. (2015) Oscillations in the human brain during walking execution, imagination and
observation. Neuropsychologia. 79(Pt B):223-32.
Ballistic-Like Residual Muscle Activation Patterns in Below-Knee Amputees
Stephanie Huang([email protected]) and Helen (He) Huang
Introduction
One important class of functions that residual muscles could restore in powered lower limb prostheses via direct
myoelectric control is feedforward postural control [1]. Feedforward postural control in lower limb prostheses
would allow the amputee user to engage more freely and safely with their surroundings. Using residual muscles
directly for feedforward postural control could require amputees to generate fast and accurate ballistic-like
muscle activation patterns using their residual muscles [2]. The purpose of this study is to see whether belowknee amputees can generate ballistic-like muscle activation patterns using their residual muscles while standing.
Methods
We recruited one bilateral below-knee amputee
(male, 56 years old). While standing wearing his
prosthesis, we asked him to control a computer
cursor using his residual gastrocnemius and
tibialis anterior muscles via proportional
myoelectric control to hit targets displayed on a
computer monitor (Fig.1). We instructed the
subject to use one short muscle burst from rest
(i.e. no muscle activation) to hit the target as
quickly as possible. We conducted two pre-test
trials where the subject hit targets in a 20-length
random sequence for each trial. Then the subject
practiced hitting each target for a total of six
minutes per target. After practicing, we conduced
two post-test trials the same as the pre-test trials.
Figure 1. Grey lines show an example of the subject’s
comfortable electromyography (EMG) activation patterns.
Four targets placed at 20% and 40% maximum voluntary
contraction (MVC) of residual muscles. Point characters
show average hit locations of pre-test and post-test trials.
Results
With a short amount of practice time, the subject
improved his ability to hit the targets more exactly
and also decreased his movement time (i.e. time
to target and back) noticeably (Fig. 1).
Conclusions
Our preliminary data suggests lightly that below-knee amputees may be able to learn how to generate effective
ballistic-like muscle activation patterns using their residual muscles for prosthesis control. This study adds to
the understanding of the functional capabilities of residual muscles, which is needed for researchers to explore
the many different ways that residual muscles can be used most effectively for prosthesis control.
References
1. Santos MJ, Kanekar N, Aruin AS (2010) The role of anticipatory postural adjustments in compensatory
control of posture: 1. Electromyographic analysis. J Electromyogr Kinesiol 20:388-397.
2. Loram ID, Lakie M (2002) Human balancing of an inverted pendulum: position control by small, ballisticlike, throw and catch movements. J Physiol 540.3:1111-1124.
Acknowledgments
This work was partly supported by NSF #1406750 & #1361549
Persistence of Reduced Neuromotor Noise in Long-term Motor Skill Learning
Meghan Huber ([email protected]), 2Nikita Kuznetsov, and 1Dagmar Sternad
1
2
University of North Carolina Greensboro, Greensboro, NC, USA
1
Acquiring a new motor skill requires long hours of practice and patience, regardless of whether the goal is to
play for the Boston Bruins or merely to make the high school hockey team. The same patience is required when
recovering from brain injury such as stroke. And yet, when motor learning is studied in the laboratory, rarely do
experimental practice sessions exceed a single hour. Long-term skill learning is marked by decrements in error
and variability, starting with relatively rapid changes and followed by subtle tuning that continues over weeks,
if not years of practice. This decrease in variability is typically ascribed to error corrections, while intrinsic
neuromotor noise is assumed to be immune to practice. The present study examined whether de novo learning
especially during the fine-tuning stage proceeds by reducing neuromotor noise.
Using a virtual throwing task, we investigated practice over 11 daily sessions (240 trials each day). One group
received a visual reward when the error was below a threshold; a control group practiced in self-guided fashion
without any reward. We expected that reward leads to faster learning and better performance, both in error and
variability. Specifically, we expected that with extended practice, the fine-tuning of skill is achieved by
decreasing the amplitude of neuromotor noise. First results showed that while reward accelerated the learning
process, the self-guided group reached the same level of performance and amplitude of noise after 11 days of
practice (Fig. 1A). Time series analyses did not detect structure different from a Gaussian noise process,
suggesting that the observed variability may be interpreted as neuromotor noise. A second experiment
demonstrated that increasing the incentive ultimately did achieve a decrease in noise amplitude, evidenced by
time series analyses. Importantly, this low level of noise persisted for five days after removing the increased
incentive, demonstrating long-term persistence of the reduced noise (Fig. 1B). A simple iterative model
illustrates how a varying noise source can account for the experimental findings (Fig.1C).
Our results suggest that subjects are sensitive to their intrinsic noise and are able to reduce it under tighter task
demands. Importantly, the reduced level of noise persisted after task demands were relaxed. These results shed
light on the long-term processes underlying neuroplasticity. Hence, have practical implications for designing
rehabilitation interventions.
*
p<.05
*
1
2
3
4
5
6
7
8
9 10 11
Days of practice
Self-guided group
Reward group
Baseline
Manipulation
Retention
12
*
10
* *
8
6
1
2
3
4
5
6
7
8
9 10 11
Release angle IQR (º)
20
18
16
14
12
10
8
6
B Reward group vs Changing-reward group C Model Simulation Results
A Self-guided group vs Reward group
11
Manipulation
Retention
10
9
8
7
6
5
Days of practice
Changing-reward group
Baseline
Simulated Reward group
1
2
3
4
5
6
7
8
9 10 11
Days of practice
Simulated Changing-reward group
Figure 1: Change in variability and noise with long-term practice of a novel virtual throwing skill. (A)
Self-guided group vs reward group. (B) Reward group vs changing reward group. All error bars represent the
±2 s.e.m. (C) Modeling of experimental results suggests that noise decreases as a function of reward.
Acknowledgements
This work was supported by the NICHD R01-HD045639, NICHD R01-HD087089-01, NSF-DMS0928587, and
NSF-EAGER 1548514.
Improving Instantaneous Cost Mapping for Predicting Human Locomotion Energetics
1
Kimberly A. Ingraham ([email protected]), 1C. David Remy, 1Daniel P. Ferris
1
Development of ‘body-in-the-loop’ optimization algorithms for minimizing metabolic energy cost during
locomotion could greatly improve the performance of robotic assistive devices (powered prostheses or
exoskeletons) [1]. These optimizations seek to minimize a physiological cost function (e.g., energy expenditure)
over a range of parameter values (e.g., controller timing) to find the optimal parameter setting [1]. Techniques to
discern the underlying energy cost-parameter relationship include instantaneous cost mapping (ICM), which
measures metabolic expenditure over a continuous sweep of parameters, and instantaneous cost gradient search
(ICGS), which estimates a local metabolic gradient at an initial parameter and iteratively steps towards a
metabolic minimum [1]. However, both these algorithms require a model of the underlying breath dynamics in
order to estimate instantaneous metabolic cost at each parameter setting. By modeling the breath dynamics as a
first-order system with a single time constant, ߬, we can reliably estimate instantaneous energetic cost from breathby-breath measurements during non-steady-state activities [2]. A common way to identify an individual subject’s
߬ is to induce an instantaneous step change in workload (e.g., increase walking speed from 1.0 m/s to 1.5 m/s)
and measure the subject’s breath-by-breath response. It is then possible to fit a first-order model to the measured
data by minimizing the sum of squared error between the model and each breath. The ߬ of the best-fit model is
the subject’s respiratory dynamic time constant. A previous study reported time constants from 20-60 seconds for
healthy humans walking on a treadmill [2]. To advance ICM and ICGS techniques, it would be helpful to know
the accuracy of our estimate of ߬, and what factors influence our ability to identify ߬ on a subject-specific basis.
We used computer simulation to examine the effects of three factors on the prediction of ߬: noise in the metabolic
measurements, magnitude of the workload step size, and the actual time constant. We created metabolic data by
simulating the underlying breath dynamics with a known ߬ and adding white Gaussian noise to the signal. We fit
a first-order model to the noisy data to estimate ߬ of the underlying signal, which was constrained between 5 and
150 seconds. We repeated this simulation 1000 times for each ߬ (20-60 sec), step size (0.18-0.93 W/kg), and
standard deviation (SD) of noise added to the signal. (0.0-0.5 W/kg). We fit a normal model to the 1000 predicted
߬values. Figure 1 depicts the confidence in the estimate of ߬, given
a noise level and step size. In practice, given some measured signal
noise, these data could be used as a lookup table to determine how
large a step is necessary to obtain a certain level of confidence in the
estimate of ߬. It is not clear yet how close our simulation would
match experimental data. The simulation indicates that a larger
workload step increases the confidence in ߬. However, inducing a
large step (e.g., stepping from no actuation to full power in a robotic
exoskeleton) may be inappropriate or unsafe for subjects as well as
introduce unwanted artifacts into the metabolic measurements (e.g.,
anticipation). It remains to be determined how much confidence in
the estimate of ߬ is required to reliably implement optimization
algorithms. Future work will focus on how estimation error Figure 1: Relationship between step size,
propagates through the system and affects the use of the algorithms noise level, and SD of the best-fit ߬ values.
during human locomotion with robotic assistive devices.
Results are shown for ߬ = 40 s.
References
1. Felt W (2015) "Body-In-The-Loop": Optimizing Device Parameters Using Measures of Inst. Energetic Cost.
PLoS One 10:e0135342.
2. Selinger J (2014) Estimating instantaneous energetic cost during non-steady state gait. J Appl Physiol
117:1406–1415.
Muscle Recruitment Synergies during Walking in an Exoskeleton are Similar across Assistance Levels
1
Daniel A. Jacobs ([email protected]), 1Jeffrey R. Koller, and 2Kathryn M. Steele, and 1Daniel P. Ferris
1
2
University of Washington, Seattle, WA, USA
Muscle recruitment data, extracted from electromyography (EMG) during motions such as walking, running, and
swimming, can be factored into a reduced set of representative signals. Investigating the structure of synergies
during walking with an ankle exoskeleton can provide insight into the neuromechanics of locomotor adaptation
during assistance [1]. Eight healthy subjects wore a bilateral, powered ankle exoskeleton and walked at 1.2 m/s
on a treadmill for 50 minutes in two conditions: unpowered and powered. We recorded surface EMG every 2
minutes and extracted muscle synergies via nonnegative matrix factorization from ten muscles in the lower limb.
Overall, six synergies were sufficient to reconstruct the muscle signal data in both conditions. The number of
synergies and the coordination of different muscles were similar to studies of walking in normal clothes [2].
Across conditions, the mean timings and weightings for each of the six synergies were significantly (Pearsons’s
centered, p < 0.05) and positively correlated (0.74 ȡ The results suggest that powered assistance
results in small changes to the motor structure from the neural standpoint despite large changes in the energetics
of locomotion [3] and suggests that a common basis may exist to describe normal walking and powered assistance.
Figure 1:
Mean timing and
weightings across subject for the
extracted synergies. Ten muscle
signals were reduced to six
synergies through nonnegative
matrix
factorization.
Muscle
Names: Soleus (SOL), tibialis
anterior (TA), peroneous longus
(PER), medial
gastrocnemius
(MG), biceps femoris long head
(BF), semitendenosis (SM), rectus
femoris (RF), vastus lateralus (VL),
vastus medialis (VM), and gluteus
maximus (GX).
References
1. Ting LH, Chiel HJ, Trumbower RD, et al (2015) Neuromechanical Principles Underlying Movement
Modularity and Their Implications for Rehabilitation. Neuron. 86(1):38-54.
2. Oliveira AS, Gizzi L, Farina D, Kersting UG (2014) Motor modules of human locomotion: influence of EMG
averaging, concatenation, and number of step cycles. Front Hum Neurosci. 8:335
3. Koller JR, Jacobs DA, Ferris DP, Remy CD (2015) Learning to walk with an adaptive gain proportional
myoelectric controller for a robotic ankle exoskeleton. J NeuroEng Rehab. 12:97
Acknowledgments
This research was funded by National Science Foundation (IIP-1026872) and by the Department of Defense
(W81XWH-09-2-0142).
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Altered plantarflexor muscle material properties in stroke survivors – does muscle stiffness influence
impaired gait?
Jakubowski, K. ([email protected]), Terman, A., Santana, R., and Lee, S.S.M.
Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, 60611
Individuals who have had a stroke have limited ankle range of motion and strength, which affects gait
kinematics and kinetics, resulting in limited mobility. In addition to impaired motor control, changes in muscle
material properties, specifically stiffness, may influence mobility. Using shear wave (SW) ultrasound
elastography, SW velocity can be used as a surrogate for muscle stiffness [1] such that SW velocity is greater in
tissue that is stiffer. To gain insight into how changes in stiffness of lower extremity muscles contribute to gait,
the aim of this study was to determine differences in the relationship between SW velocity and ankle positions
between the paretic and non-paretic side of ankle plantarflexors (medial gastrocnemius, MG) and dorsiflexors
(tibialis anterior, TA) and relate them to gait parameters and joint kinematics and kinetics during gait.
Fourteen stroke survivors participated in this study (age:60.1r5.9yrs; height:1.68r0.09m; body
mass:77.6r12.5kg; time post-stroke:10.6r7.3yrs.). Subjects were seated with their knee in maximum extension
and their foot secured to a platform of a dynamometer (System3Pro, Biodex). B-mode and SW elastography
ultrasound images and measurements (Aixplorer, SuperSonic Imagine) of MG and TA muscles were captured,
as well as joint angle and torque at different ankle angles (90q, 15q plantarflexion (PF), maximum dorsiflexion
(DF), maximum PF, and two other intermediary angles) while the muscle was passive. Gait analysis was
conducted on nine of these individuals during over ground gait at their preferred gait velocity without assistive
devices (ten camera motion capture system (Qualysis, Gothenburg, Sweden), standard 30 reflective marker set
on torso, pelvis, and lower limbs, five force plates (AMTI, Watertown, MA)). Parameters where analyzed at
heel strike (HS), toe off (TO) in the sagittal plane during stance phase.
Our main findings show that SW velocity of the MG increase non-linearly, while the TA decreases non-linearly
(quadratic fits of 0.90r0.14 and 0.89r0.19) from PF to DF. In addition to the SW velocity of the paretic MG
muscle being on average 27.7% greater (p = 0.021) than the non-paretic side at 90q, the SW velocity was also
significantly greater, on average 26.8% at 15q PF (p=0.008), and 28.1% at the max DF (SW value extrapolated
from quadratic fit so that ankle angle is matched to paretic side, p=0.05). We found significant differences in
temporal gait parameters, kinematics, and kinetics, such as increased stance phase, greater ankle moment and
power, and more positive work on the non-paretic limb. More importantly, there were correlations between SW
velocity of the MG measured at 90q and max DF with stride length (90q degrees, r2=0.475, p=0.04; max DF,
r2=0.479, p=0.039) and ankle power at TO (90q, r2=0.497, p=0.034; max DF, r2=0.574, p=0.018).
At TO, activation of the MG is crucial for generating force to achieve sufficient power for push off and to
propel the center of mass forward. Having to overcome the increased stiffness in combination with decreased
motor control and strength at specific events during gait would certainly exacerbate any deficiencies. These
results have strong implications that the passive stiffness of the MG affects the gait of an individual who has
had a stroke. Patient specific information on muscle material properties, like stiffness, that affect gait, would
allow clinicians to refine rehabilitation to specifically address decreasing muscle stiffness.
References
1. Bercoff J et al. (2004) Supersonic shear imaging: a new technique for soft tissue elasticity mapping.
Ultrason, Ferroelectr and Freq Control, IEEE Transactions 51:396-409.
Acknowledgments
This work was funded by NIH K12HD073945.
1
Metabolic and muscle activity during walking up- and down-hill using a powered leg prosthesis
Jana R. Jeffers ([email protected]), 1Caroline D. Wilson ([email protected]), and
1,2
Alena M. Grabowski ([email protected])
1
University of Colorado, Boulder, CO, USA, 2 VA Eastern CO Healthcare System, Denver, CO, USA
When people with unilateral trans-tibial amputation (TTA) use a passive prosthesis to walk on level ground,
they consume 30% more metabolic energy than non-amputees (NA) at the same speeds [1] and exhibit
asymmetrical muscle activity magnitude and duration between affected and unaffected legs [2]. However, when
TTA use a powered prosthesis to walk on level ground, their metabolic demands are nearly the same as those of
NA [1] but the effects on muscle activity are not known. Activities of daily living include negotiating up- and
down-hill slopes, but it is unclear how the use of a passive or powered prosthesis affects metabolic demand
during up- and down-hill walking. We investigated the changes in metabolic cost and muscle activity of both
legs in TTA using passive and powered prostheses
during level and hill walking.
Nineteen NA subjects (13M 6F, 29±8.7 yrs) and three
TTA subjects (1M 2F, 45.7±3.2 yrs) walked 1.25 m/s on
a dual-belt force treadmill (Bertec Corp., Columbus, OH)
on 7 slopes (0º, ±3°, ±6°, ±9°) using their own passive
prosthesis (ESAR) and a powered prosthesis (BiOM,
BionX Medical Tech. Inc., Bedford, MA). Subjects
walked on each slope for 5 minutes while we measured
metabolic rates via indirect calorimetry (ParvoMedics,
Sandy, UT) and muscle activity from 13 muscles using
surface EMG (Noraxon, Scottsdale, AZ). We averaged
1: Net metabolic COT across slopes (deg) for NA and
metabolic data from the last 2 min of each trial and Figure
TTA using the BiOM and ESAR prostheses. *Only 1 TTA
subtracted standing from gross metabolic rate to obtain completed the trial at +6° and none completed the trial at +9°.
net values. We converted to cost of transport (COT)
using a standard equation [3]. We used a custom Matlab (Mathworks, Natick, MA) script to analyze EMG data.
We applied a 10-495 Hz band-pass filter, rectified, and then used a RMS-average filter with a 50ms window.
We normalized EMG to the maximum signal magnitude per stride for each muscle during level-ground walking
and averaged integrated EMG data (iEMG) over 10 strides. We used a MANOVA to compare COT and iEMG
with slope and prosthesis as independent variables and used Bonferroni-corrected post-hoc t-tests to determine
differences between use of prostheses and NA data (COT only) for each slope.
Net COT was different across slopes (p<0.05) but not different between prostheses (Fig. 1). Net COT was
highest at +9° for all subjects and lowest at -6° for NA and at -3° for TTA using both the BiOM and ESAR.
There were no interactions between slope and prosthesis. iEMG of the unaffected leg soleus, gluteus maximus
(UGmax), and biceps femoris were greater on uphill and lower on downhill slopes (p<0.05). UGmax activity
was significantly greater at +9° compared to 0° and -9°.
Our hypothesis that use of the BiOM would result in lower net metabolic cost was not supported for all slopes.
Because unaffected leg EMG activity showed a dependence on slope but not prosthesis, our second hypothesis
was not supported. However, we intend to improve our statistical power by including more TTA subjects in the
future.
References
1. Herr, H.M, Grabowski, A.M., Proceedings of Biological Sciences, 279 457-64, 2012
2. Isakov, E., Keren, O., Benjuya, N., Prosthetics Orthotics International, 24 216-220, 2000
3. Brockway, J. M., Human Nutrition: Clinical Nutrition, 41C 463-471, 1987
1
Leg Joint Function During Walking and Running Maneuvers
Devin Jindrich ([email protected]) and 2Mu Qiao ([email protected])
1
California State University, San Marcos, CA, USA
2
Arizona State University, Tempe, AZ, USA
Walking and running are often characterized as having distinct leg mechanics: whereas in walking the leg acts
as a stiff strut, in running the leg acts more like a spring. However, whether individual joints exhibit strut-like or
spring-like mechanics similar to the overall leg is unclear. Joints could also potentially produce power (like
motors) or absorb energy (like dampers). Moreover, during unsteady locomotion, energy production or
absorption may be necessary to maintain stability or to maneuver.
We tested the hypothesis that the hip, knee, and ankle do not act solely as struts or springs, but exhibit different
mechanical functions during both constant-average-velocity (CAV) locomotion and during maneuvers.
Specifically, we hypothesized that the hip functioned as a motor because it has relatively long muscles and short
tendons, whereas the ankle functioned as a torsional spring because it has relatively short muscle fibers and long
tendons. Finally, we hypothesized that the knee acted as a strut to transfer energy proximo-distally.
We asked sixteen male participants (age = 27±4 years; body mass = 70±8 kg; body height = 177±7 cm,
mean±s.d.) to walk (1.5±0.2m·s-1) and run (3.0±0.3m·s-1) in 3 conditions: constant-average-velocity (CAV),
accelerating (ACC) and decelerating (DEC). We collected kinematics and ground-reaction forces, and used
inverse dynamics to calculate net joint moments.
To characterize leg joint function, we developed strut, spring, motor, and damper indices[1]. The strut index
(STR) was the dimensionless ratio between joint power (Pjoint) and moment (Mjoint). An STR of 100% means the
joint transfers energy between two segments; an STR of 0 means that the joint produces or absorbs energy. The
motor (MT), spring (SPR) and damper (DP) indices characterize the consequences of joint work, Wjoint. MT
represents work production, SPR the potential for storage and return, and DP work absorption.
WALKING
RUNNING
Figure 1: Functional indices for the leg joints during walking (left panel) and running (right panel). Mean±s.d. error.
We found that the leg joints showed distinct mechanical functions for both walking and running, during both
steady and unsteady locomotion tasks (Fig. 1). The hip was a power producing motor, and ankle was a torsional
spring. The knee did not act as a strut, but as a strut, motor, and damper. Although leg function is commonly
thought of as fundamentally distinct for walking and running, joint function was consistent for both gaits. Jointlevel functional analysis could contribute to gait evaluation, rehabilitation, designing prostheses,
neuroprostheses, exoskeletons and legged robots.
References
1. M. Qiao and D. L. Jindrich. Leg Joint Function During Walking Acceleration and Deceleration. Journal of
Biomechanics Jan 4;49(1):66-72.
Acknowledgments
We are grateful to Prof. James Abbas for using the facilities at the Center for Adaptive Neural Systems.
1
Improving Usability and Acceptance of Arm Rehabilitation Robotics: Development of ARMin V
Fabian Just ([email protected]), 1Kilian Baur, 1Robert Riener, 1Verena Klamroth-Marganska and
1,2
Georg Rauter
1
ETH Zurich and University Hospital Balgrist, Zurich, Switzerland
2
University of Basel, Basel, Switzerland
Robots are applied in therapy of stroke patients to restore lost motor function. Over the past years, the use of
robots in therapy has been constantly increasing due to the following advantages of robotic rehabilitation
therapy over conventional manual therapy: Robots enable high intensity training through an increased training
duration and a large number of movement repetitions, while the therapists are relieved from physically
exhausting workload. Importantly, high intensity training is believed to provide increased gains in motor
function for patients. However, these gains have not reached a magnitude, yet, that would indicate a clear
advantage for the patient. In our opinion, there is still large unexploited potential for improving therapy results
by improving robot usability. To provide an example, therapists are trained to manually interact with the
patients, while robots now impede such a direct haptic interaction. Therefore, therapists get the feeling that they
are required to handle devices instead of treating patients. Consequently, there seems to remain a considerable
potential to increase acceptance of robotic therapy and thereby to further boost therapy outcomes. We think that
rehabilitation robots should become simpler and more intuitive to use.
To account for this demand of improved robot usability, we have developed an online adaptive compensation
(OAC) for the ARMin rehabilitation robot, an actuated arm exoskeleton robot with seven degrees of freedom
[1]. In a recent study, we were able to show that ARMin therapy entails significantly higher functional
improvements in moderately to severely affected chronic stroke patients than conventional therapy [2]. We are
convinced that improved usability will add up to even higher functional improvements for the patients.
Therefore, in the new version, ARMin V, robot adjustment is fully automated via OAC to improve usability for
the therapist. The OAC integrates information on upper and lower arm lengths as well as adaptation of shoulder
angle settings to fit the patient’s anthropometry. In this way, the OAC enables improved transparency of the
robot even at the border of the workspace (Figure 1).
In return, improved transparency is the basis for
successful haptic patient and therapist interaction, i.e.
the therapist can feel the patient’s arm more accurately
and teach the robot how to account for spasms or other
movement disorders.
Figure 1: The arm elevation axis 1 [1] is moved from
an initial angle of 90° to a desired angle of 45° using a
non-linear PD controller. As soon as 45° are reached,
the controller is faded out within 0.1 s. Optimal
behavior corresponds to holding the desired position.
The OAC succeeded in all our measurements leading to a precise robot compensation for the entire workspace
and even for extreme arm anthropometries. Due to the promising results, the OAC will be integrated in all
ARMin devices.
References
1. Riener R (2011) Transferring ARMin to the Clinics and Industry. Topics in Spinal Cord Injury
Rehabilitation 17.1:54-59.
2. Klamroth-Marganska V (2014) Three-Dimensional, Task-Specific Robot Therapy of the Arm after Stroke:
A Multicentre, Parallel-Group Randomised Trial. The Lancet Neurology 13.2:159–166
Acknowledgments
This work was supported by ETH research grant 0-20075-15 and the CRRP Neuro-Rehab (UZH)
Kinematic Differences between Sides in Individuals with Unilateral Hip Pain during Single Leg Squat
and Step Down Tasks
Anne Khuu ([email protected]), Kari L. Loverro, and Cara L. Lewis
College of Health and Rehabilitation Sciences: Sargent College, Boston University, Boston, MA, USA
Dynamic tasks that isolate a single limb, such as the single leg squat (SLS) and step down (SD) tasks, may be
better able to identify asymmetrical movement patterns and limb-specific neuromuscular control deficits than
bilateral tasks [1]. In individuals 1 to 2 years after hip arthroscopy for intra-articular hip pathology, Charlton et
al. found that the surgical limb had greater pelvic obliquity than the nonsurgical limb during single leg stance
prior to starting a SLS [2]. Since Charlton et al. used 2-dimensional video analysis and were restricted to frontal
view measures, it may be useful to further examine the kinematics of individuals with hip pathology during
single leg functional tasks using 3-dimensional measures. Therefore, the purpose of this study was to examine
kinematic differences in the trunk, pelvis, and lower extremity in all planes between the affected side and the
unaffected side in individuals with unilateral hip pain (UHP) during 2 single leg functional tasks: SLS and SD.
Twenty individuals with UHP (females = 12, males = 8; age 29.7 ± 9.3 years; height 1.72 ± 0.11 m; mass 73.3 ±
15.1 kg; UCLA activity score 8.2 ± 2.6; positive anterior impingement test 60%; diagnosis of femoroacetabular
impingement and/or labral tear 65%) provided informed consent and participated in this study. Threedimensional kinematic data of the trunk, pelvis, hip, knee, and ankle were collected using a motion capture
system (VICON®) while participants performed the SLS and SD tasks. For the SLS, participants stood on both
feet with their arms by or out to their sides, shifted their weight onto one leg, held their non-stance knee in 90º
of flexion while keeping their non-stance thigh vertical, squatted as low as possible in a controlled manner, and
returned to the starting position. For the SD task, participants stood with both feet on top of a wooden box 16
cm tall, lowered their non-stance leg until their heel lightly touched the floor, and returned to the starting
position. Each task was performed five times on each leg. Visual3D (C-Motion, Inc.) was used to calculate
trunk and pelvic segment angles and hip, knee, and ankle joint angles. Paired t-tests were used to compare
trunk, pelvic, hip, knee, and ankle angles at peak knee flexion (PKF) and 60º of knee flexion (60KF) between
the affected side and the unaffected side.
No differences were found between the affected side and the unaffected side for the SLS at either of the analysis
points. For the SD, the affected side had 1.8º greater trunk flexion, 2.8º greater hip flexion, and 2.2º greater knee
flexion than the unaffected side at PKF (p ≤ 0.026). At 60KF, the affected side had 1.2º greater trunk flexion
than the unaffected side (p = 0.048).
Individuals with UHP use a different movement strategy on their affected side to accomplish the same goal (i.e.,
touching their heel to the floor from a 16 cm step) than on their unaffected side during the SD. Our findings
suggest that the SD may be more sensitive to differences between the affected side and the unaffected side in
the sagittal plane in individuals with UHP than the SLS. In addition, kinematics differences between sides in
individuals with UHP during the SD may be more pronounced at PKF than at an intermediate degree of knee
flexion.
References
Myer GD (2011) Utilization of modified NFL combine testing to identify functional deficits in athletes
following ACL reconstruction. J Orthop Sports Phys Ther 41:377–387.
2. Charlton PC (2015) Single-leg squat performance is impaired 1 to 2 years after hip arthroscopy. PM&R, In
Press. doi:10.1016/j.pmrj.2015.07.004
1.
Acknowledgments
Supported by NIH NIAMS R21 AR061690 and K23 AR063235.
1,2
The “Beam Me In” Strategy
Verena Klamroth-Marganska ([email protected]), 1,2Kilian Baur, 3Nina Rohrbach, and 1,2Robert
Riener
1
Sensory Motor Systems Lab, ETH Zurich, Switzerland
2
University Hospital The Balgrist, Zurich, Switzerland
3
Human Movement Science, Technical University Munich, Germany
Introduction: Telerehabilitation is the ability to provide distant support, evaluation and intervention to disabled
persons via telecommunication. Most telerehabilitation is highly visual. We present a telerehabilitation system
that does not only allow for haptic intervention (physical therapy) from distance but may provide a completely
new way of haptic evaluation as it enables the therapist to feel the patient’s motor performance on the therapist
arm („Beam me in“). It is realized by use of ARMin, an exoskeleton robot for neurorehabilitation therapy of the
arm [1]. Two ARMin devices are necessary: The affected arm of a patient (e.g. after stroke) is placed in one
ARMin, the therapist arm is placed in the other device. A bidirectional teleoperation control strategy (i.e., the
master-slave system [2]) allows two configurations: In the slave configuration, the therapist in the “master
ARMin” describes with his arm different trajectories that are followed by the patient arm in the “slave ARMin”.
The interaction torques between the patient arm and the “slave ARMin” are transferred to the therapist in the
“master ARMin”. Thus, the therapist can feel how active, passive or resistant the patient is to the movement
imposed. This slave configuration should enable the therapist to feel the patient reaction (e.g., spasticity) to a
described movement. In the master configuration, the roles are switched: The patient arm in the “master
ARMin” moves and thus, guides the therapist arm in the “slave ARMin”. The therapist can either behave
passive to assess the patients’ movement, or actively follow and thus support the patient, or provide resistance.
The master configuration should enable the therapist to assess the patient movement (e.g. active range of
motion) in his own arm. We tested whether “Beam me in” enables therapists to feel the patient’s motor
performance on the own arm and, thus, could serve as a medium to provide insights into the clinical picture of
motor function.
Methods: Eleven physical and four occupational therapists (14 female; mean age of 30.4 years, standard
deviation SD 7.9, range 22-51) with a mean professional experience of 5.1 years (SD 5.2, range 0-15) agreed to
participate. Therapists were placed in one ARMin robot and performed movements in master and slave
configurations with recorded and simulated stroke patients’ symptoms. Therapists assessed the resistance to
passive movement (RPM) of elbow flexion-extension by applying the Modified Tardieu Scale (MTS) and rated
pathological synergies for arm elevation. They were asked to rate the sessions by evaluating statements about
the “Beam Me In” strategy on a six-point Likert scale (1. “Beam me In" is an appropriate tool to gain insights
into the clinical picture of a patient. 2: “Beam me In” enables a new way of therapist-patient interaction).
Results: We found excellent inter-rater reliabilities for the MTS score and the rating of the pathological
synergies. Thirteen out of 15 therapists agreed that “Beam me In" is an appropriate tool to gain insights into the
clinical picture of a patient. All 15 therapists agreed that “Beam me In” enables a new way of therapist-patient
interaction.
Discussion: Therapists showed an overall positive attitude towards the “Beam me In” concept and could rate
motor performance without being in physical contact with a patient. Though it is conceptually more intensive
requiring a second robotic device, we believe that the “Beam Me In” strategy can be successfully used for
telerehabilitation and offers a new method in neurorehabilitation therapy.
1.
2.
Nef, T., M. Guidali, and R. Riener, ARMin III - arm therapy exoskeleton with an ergonomic shoulder actuation. Applied
Bionics and Biomechanics, 2009. 6(2): p. 127-142.
Lanini, J., et al. Teleoperation of two six-degree-of-freedom arm rehabilitation exoskeletons. in Rehabilitation Robotics
(ICORR), 2015 IEEE International Conference on. 2015. IEEE.
A Novel Approach to Solve Predictive Simulations in a Stochastic Environment
Anne D. Koelewijn ([email protected]) and Antonie J. van den Bogert
Cleveland State University, Cleveland, OH, USA
Predictive simulations of human movement, such as walking [1], do not predict all features when minimizing
muscular effort. These simulations ignore the noise in the system and solve the problem in a deterministic
environment, which does not yield the optimal solution for a stochastic nonlinear system, such as a human with
muscles. Recent studies suggest that noise helps to explain certain human movement strategies (e.g. [2]). Thus,
predictive simulations may better reflect human movement when taking into account noise. However, trajectory
optimization of stochastic nonlinear systems has been solved only for certain special cases (e.g. [4]).
In this abstract we propose a new approach to optimize a trajectory in a stochastic environment, using direct
collocation. Then, multiple episodes of some task, each with noise, are optimized. The controller consists of timedependent open-loop control with feedback. The total effort is minimized over all episodes. Using direct
collocation, the decision variables are the states at all time points of all episodes, and the controller parameters.
This concept is demonstrated on a pendulum swing-up problem. The pendulum has one degree-of-freedom, the
angle between the ground and the pendulum. The torque at the base controls the pendulum. Noise is added to the
angular acceleration. The objective is to swing the pendulum up in 10 seconds, minimizing the squared torque.
Figure 1 shows the optimal swing-up for different levels of variance.
One can see that the swing-up occurs later with increased noise
variance. This is expected, because more control torque is required
to keep the pendulum in this unstable equilibrium in a noisy
environment, so less time is spend near the upright position.
Figure 1 - Optimal trajectories that were found with
different noise levels. With increasing noise, the final
swing-up occurs later in time.
We also show that co-contraction is optimal for certain tasks that
minimize effort. To do so, two same Hill-type muscles are used to
control the pendulum. The objective is to keep the pendulum upright
in a noisy environment. Three control parameters are optimized in
this symmetric problem, an open-loop control input, the cocontraction, and a position and derivative feedback gain.
This novel approach for predictive simulations of human movements will be used in predictive simulations of
walking. Co-contraction of muscles can then be predicted, for example in the upper leg of a below-knee amputee.
Also, prostheses and exoskeletons can benefit from this approach. A controller with muscle-like behavior can be
optimized using this approach, such that it uses stabilizing muscle properties to minimize required torque.
References
1. Ackermann M and van den Bogert AJ (2010). Optimality principles for model-based prediction of
human gait. J Biomech 43-6:1055–1060.
2. Donelan JM et al. (2004). Mechanical and metabolic requirements for active lateral stabilization in
human walking. J Biomech 37-6: 827–835.
3. Todorov E (2011). Finding the most likely trajectories of optimally-controlled stochastic systems. IFAC
18: 4728–4734.
Acknowledgments
This research was supported by the National Science Foundation under Grant No. 1344954 and by a graduate
scholarship from the Parker-Hannifin cooperation.
1
Force field adaptation using computational model without trajectory planning
Yasuharu Koike ([email protected]), 1 Hiroyuki Kambara and 1 Natsue Yoshimura
1
Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
The end point control hypothesis was rejected by Bizzi’s experiment [1], but if the existence of a forward
dynamics model is assumed, a hypothesis which does not require trajectory planning is still attractive. The CNS
learns how to generate reaching movements toward various targets in the workspace. However, it is difficult to
perform various movements with high accuracy using a single feedback controller. Since the gravitational force
acting on the arm depends on the posture of the arm, the force required to hold the hand at the target varies with
the target position. For these reasons, there is no guarantee that a single feedback controller trained for a
particular target would generate accurate reaching movements to other targets. Here we introduce an additional
controller called an inverse statics model, which supports the feedback controller in generating reaching
movements toward various targets. It handles the static component of the inverse dynamics of the arm. That is,
it transforms a desired position (or posture) into a set of motor commands that leads the hand to the desired
position and holds it there. Note that the arm converges to a certain equilibrium posture when a constant set of
motor commands is sent to the muscles because of the spring-like properties of the musculoskeletal system.
However, there are many combinations of flexor and extensor muscle activation levels to achieve the same
equilibrium position. This means that a constraint condition is needed for the inverse statics model. If the
inverse statics model is trained properly, it can compensate for the static forces (e.g. gravity) at the target point.
Therefore, accurate reaching movements toward various target points are realized by combining the inverse
statics model and the feedback controller which works moderately well within the workspace (Fig.1A)[2].
B
Inverse Static Model
Muscle
activation
Vertical Direction
Sagittal Plane
θ2
elbow
a5
a1
a6
a2
4
6
2
Horizontal Direction
shoulder
+
Feedback controller
-
a7
a3
a4 a8
3
5
1
Arm Model
θ1
Y position[m]
A
0.5
0.4
NullForce
Before
After
aftereffect
Target position
Forward Dynamics Model
0.3
-0.1 -0.05 0
0.05 0.1
X position[m]
Figure 1: Force field adaptation result using computational learning-controlling model with musculo-skeletal
model. Black, green, red and blue lines indicate null force, the beginning of learning, after adaptation, and after
effect trajectory (without force field after adaptation), respectively.
This computational learning-controlling model can be used to learn the dynamics of the environment, such as
force field without any prior knowledge. So velocity force filed was applied to this model, and the result is
shown in Fig.1 B. As shown in Fig.1 B, the trajectory converged to the straight line without a trajectory
planning (green to red line). This is first demonstration to show learning result for force field adaptation without
trajectory planning.
References
1. Bizzi, E., Accornero, N., Chapple, W., & Hogan, N. (1984). Posture control and trajectory formation during
arm movement. The Journal of Neuroscience, 4(11), 2738–2744.
2. Kambara, H., Kim, K., Shin, D., Sato, M., & Koike, Y. (2009). Learning and generation of goal-directed
arm reaching from scratch. Neural Networks, 22(4), 348–361.
Acknowledgments
Supported by MEXT KAKENHI Grant Numbers 26112004 and SRPBS from MEXT and AMED.
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Length dependence of shear elastic properties in hypertonic and contralateral biceps brachii after
hemispheric stroke; an ultrasound based analysis
1
Andrew Lai ([email protected]), 1Nina Suresh, 2Xiaogang Hu and 1W Zev Rymer
1
2
University of North Carolina, Chapel Hill, NC, USA
Muscular hypertonia is a motor control disorder that results from many types of brain injury, including
hemispheric stroke. The increase in tone is usually attributed to neural reflex mechanisms, however, non-neural
mechanisms may play a major role in mediating clinical hypertonia [1], and they are relatively uninvestigated.
Currently we are examining the hypothesis that non-neural factors play a major role in mediating clinical
hypertonia [2]; specifically, we hypothesize that alterations in muscle’s elastic properties contribute to
hypertonia. Accordingly, the goal of this project was to determine if the passive muscle in spastic/paretic limbs
has altered elastic properties, and whether these properties change systematically with changes in muscle length.
Shear wave elastography (SWE) was used to estimate the elastic properties of spastic-paretic and contralateral
biceps brachii over a range of elbow flexion angles. In 11 hemispheric stroke survivors (age 61±8.9, 5 male/ 6
female), Shear wave velocity (SWV) was measured in the lateral muscle belly while concurrent EMG activity
was monitored on the medial aspect of the muscle belly to identify trials with unwanted muscle activity.
In order to compare the affected biceps with the contralateral biceps, shear wave velocity readings were binned
by elbow flexion angle (bin size: 10 degrees). At a given subject’s end range of motion, 9/11 subjects had
significantly higher SWV on the affected biceps. At the end range of motion, mean difference in SWV was
0.56±0.62 m/s (Figure 1). At joint angles less than maximum, SWV on the affected side was significantly
greater in 11/29 bins. The mean difference in SWV (affected-contralateral) was 0.11±0.60 m/s (Figure 1). To
characterize the relationship between SWV and elbow flexion, both straight-line and exponential functions were
fit to the data. The slope of the affected biceps fit was greater by 86%±50% compared to the contralateral for
8/11 subjects. The exponential shape parameter (b in aebt) of the affected biceps exponential fit was greater by
60%±55% compared to the contralateral for 9/11
subjects.
These results indicate that the stroke-affected biceps
may have different mechanical properties in many
stroke survivors and that the differences become
more pronounced as the biceps muscle is lengthened.
Though we are currently unable to distinguish the
cause of the alterations, it is possible that changes in
connective tissue matrix play a role in altering the
mechanical properties of stroke muscle. These
changes in extracellular matrix could be mediated by
alterations in muscle stem cell behavior (satellite
cells), or as a result of motoneuron death after stroke.
References
1.
2.
Lee, S.S.M., S. Spear, and W.Z. Rymer, Quantifying changes in
material properties of stroke-impaired muscle. Clinical
Biomechanics, 2015. 30(3): p. 269-275.
Gracies, J.M., Pathophysiology of spastic paresis. I: Paresis and soft
tissue changes. Muscle Nerve, 2005. 31(5): p. 535-51.
Figure 1: Shear wave velocity differences (affectedcontralateral) at different elbow flexion angles. (A)
At angles less than the end range of motion, SWV
was significantly higher on the affected side in 11/25
bins (mean 0.11±0.60 m/s). (B) At the end range of
motion, SWV was significantly higher in the affected
side in 9/11 subjects (mean 0.56±0.62 m/s).
Acknowledgments
This work was supported by NIH T32 HD07418 and a Davee Foundation grant (PI Suresh).
Fitts’ Law Assessment in Full Body Movements to Virtual Targets
Sam Leitkam ([email protected]), Megan Applegate, Alexa Hoynacke and James Thomas
Ohio University, Athens, OH, USA
Fitts’ Law states that for a given target, the size and distance of the target from the end-effector maps to the
amount of time and accuracy with which a person can reach the target [1]. Variations of this have been shown to
hold true computer mousing tasks, seated arm reaches, and leg pointing motions [2]. However, no data are
available to evaluate how this relationship changes when other factors in human movement are included, such as
maintaining balance during reaches that require trunk displacement.
This study sought to determine if reaching tasks that necessitated significant trunk displacement altered the
relationship between movement time and size of targets defined by Fitts’ Law. Nineteen healthy participants (12
men, 7 women) completed reaches to four virtual target locations in a fully immersive virtual reality environment.
The target locations were normalized to the subject’s anthropometric characteristics such that the levels of
movement corresponded to 0, 15, 30, and 60 degrees of theoretical lumbar flexion with an outstretched arm. The
sizes of the virtual targets were adjusted such that the index of difficulty (ID) was constant for all targets within
a single block of trials. The equation for ID as described in Fitts’ Law is shown in equation (1) where A is the
distance to the target and W is the width of the target. A diagram of the target locations, orientations, and sizes is
shown in Fig. 1 where A was calculated as the distance from the head in standing position to the target and W
was the diameter of the target. Participants completed five reaches to each target location and ID (i.e. 3, 5, and
7) for a total of 60 reaches.
(1)
ID = log2(2A/W)
In order to satisfy Fitts’ Law,
participants were asked to reach
and touch the center of the targets
as quickly and accurately as they
could.
A mixed linear model was used
for statistical analysis of the
Figure 1. Diagram of target locations and orientations for 0, 15, 30, and 60
movement time while assessing
degrees of lumbar flexion at ID=5. The width of each target, W, was scaled to
main effects of ID and target
maintain the ID for each distance A.
location. As expected, average
movement time increased from 0.97s (ID=3) to 1.25s (ID=7) as a function of ID (p<0.05). However, average
movement time also increased from 0.97s (0° target) to 1.30s (60° target) as a function of target location (p<0.05).
There was no significant interaction effect between target location and ID.
The finding that the ID had a significant effect was expected and is consistent with Fitts’ Law. However, the
finding that target location had a significant effect indicated that movements that required larger trunk
displacements took longer even though ID was constant across target locations. While Fitts’ Law has been shown
to be quite robust for many upper and lower extremity tasks, this finding suggests that it does not hold true for
reaching movements that necessitate large trunk displacement. Therefore, the role of trunk movement on the
Fitts’ Law relationship needs to be examined further in order to fully quantify the biomechanical and neural
components controlling this movement process.
References
1. Fitts PM (1954) The information capacity of the human motor system in controlling the amplitude of
movement. J. Exp. Psych 47(6) 381-391
2. Hoffmann ER (1991) A comparison of hand foot movement times. Ergonomics 34(4) 397-406
Empirical investigation and mathematical modeling of energetics and mechanics of skeletal muscle
1
Lemaire KK ([email protected]), 2van der Laarse WJ, 1Kistemaker DA, 1Jaspers RT and 1van Soest AJ
1
Department of Human Movement Sciences, VU University, Amsterdam, The Netherlands
2
Department of Physiology, VU University medical Center, Amsterdam, The Netherlands.
To investigate the role of metabolic energy consumption in motor control, musculoskeletal models that yield
adequate predictions of both mechanics and energetics are indispensable tools. Current musculoskeletal models
rely on the phenomenological Hill model, which lacks a direct relation between mechanics and energetics. In
the Huxley model, this relation is an integral part of the model. In this study, we present preliminary results
regarding the evaluation of the validity of a structural, Huxley type muscle-tendon complex model based on a
dedicated, comprehensive dataset of the mechanics and energetics of mouse m. soleus fiber bundles (n=6).
Freshly dissected muscle fiber bundles (~50 fibers) were suspended in a glass, jacketed chamber filled with
oxygenated Tyrode solution, which was maintained at 32 ºC. The distal tendon was connected to a servo
controlled motor in series with a force transducer, via a tungsten wire which left the chamber through a thin
capillary. Stimulus pulses were applied directly to the medium to elicit muscle contraction. Oxygen
concentration in the solution was measured with a polarographic oxygen electrode. This unique setup allowed
for full control of stimulation and fiber bundle length change, while tendon force and oxygen consumption were
measured [1]. The bundles were subjected to sinusoidal movements with stimulation occurring either during
shortening or during lengthening, for a time period of 3 minutes. Interspersed between these longer trials, the
bundles were subjected to short-duration isometric and dynamic contractions with varying contraction
conditions; data from these trials were used to fully characterize the mechanical behavior of the bundles.
Finally, the 3 min trials were repeated after cross-bridges inactivation by blebbistatin [2]. The latter allowed
quantification of the fraction of metabolic energy expenditure associated with cross-bridge cycling, in relation
to the total energy metabolic expenditure. Modifications to the classic 2-state Huxley model were made to
include series and parallel elasticity, an active force-length relationship and activation dynamics [3]. Energy
consumption in the model was dependent on both cross-bridge cycling and active state. Parameters for the
model were partly obtained from literature, and partly fitted on a randomly selected subset of trials. Simulations
were made of the remaining trials, and the simulation results were compared to the experimental data, as a
measure of model validity.
Overall mean concentric and eccentric efficiency of muscle contraction (mean (SD)) was 0.16 (0.03) and -1.25
(0.04), at 3.32 (1.3) and -3.38 (1.3) W/kg, respectively. The corresponding relative contribution of cross bridge
cycling to total energy expenditure was 0.68 (0.05) and 0.46 (0.04), respectively. For a typical example, the
overall root mean squared error between the simulated and the experimental force traces were ~ 1% and 1.5% of
maximal isometric force, for simulations of the fitted and the non-fitted datasets, respectively. These values are
similar to those found in [3]. Analysis of model energetics is ongoing.
The model presented here can be readily implemented in large-scale musculoskeletal modeling. Pending
ongoing validation of the model with respect to energetics, this may result in musculoskeletal models in which a
more direct relation between mechanics and metabolic energy expenditure is featured. The latter will provide
substantial contributions to studies investigating the role of metabolic energy consumption in motor control.
References
1. Wong YY, Handoko ML, Mouchaers KT, de Man FS, Vonk-Noordegraaf A, van der Laarse WJ. Am J
Physiol Heart Circ Physiol. 2010 Apr;298(4):H1190-7.
2. Straight AF, Cheung A, Limouze J, Chen I, Westwood NJ, Sellers JR, and Mitchison TJ (2003) Science 14:
1743-47.
3. Lemaire KK, Baan GC, Jaspers RT and van Soest AJ (2016) J Exp Biol in press doi:10.1242/jeb.128280
Sagittal Joint Power during Steps Leading up to Walk-to-Run Transition
1
Li Li ([email protected]), 2Jiahao Pan, and 3Shuqi Zhang
1
Georgia Southern University, Statesboro, GA, USA
2
Shanghai Sport University, Shanghai, China
3
Northern Illinois University, Statesboro, IL, USA
Human movements are studied mostly either in stable states, such as quite standing, or assumed stable states, i.e.,
repetition following the same pattern. There are ample examples of unstable motion among daily living. We have
studied walk-to-run gait transition as representation on how we move from one stable state to another. Data
collection conducted on an instrumented treadmill (AMTI, Inc., USA) with motion-capture system (VICON, Ltd.,
UK). Gait transition induced among 13 college aged participants while locomotion speed gradually increased
starting from a slow walking speed. Joint power of the last five steps before walk-to-run transition was estimated
via inverse dynamics. Ensemble curves of hip, knee and ankle joint power are presented in the following figure,
where positive values are ankle plantar flexion with knee & hip extension. Horizontal axes represent 100% of
stance phase. Non-linear changes with each of the five steps among the all three joints can be observed.
Our observations indicates that 1). Walk-to-run transition can be studied when locomotion speed was changing;
2). Gait transition is not an instantaneous event but an event associated with changing joint power before the
actual gait change; 3). Quantitative change lead to pattern changes as approaching to the final transition step; and
4). All three lower extremity joints were involved in the preparation of gait transition.
Toward the Practical Application of Crossbridge Muscle Models
1
David Lin ([email protected]) and 1Sampath Gollapudi
1
Washington State University, Pullman, WA, USA
Introduction: Mechanistic-based crossbridge models of muscle contraction were first formulated over fifty years
ago but have never gained traction as a valuable tool in musculoskeletal modeling. Two major obstacles for the
practical application of cross-bridge models have prevented their incorporation into musculoskeletal models: the
parameters of model are largely tied to molecular interactions, which are difficult to estimate for intact in vivo
muscle, particularly human muscle; and the number of model parameters is relatively large.
Objective: We have developed a methodology to estimate crossbridge model parameters for in vivo muscle from
in vitro single fiber experimental measurements. Our objective is to reduce the number of parameters (to make
parameter estimation tractable) through sensitivity analyses and still maintain model accuracy.
Methods: We had obtained previously shortening and lengthening force-velocity (F-V) data from human single
type I (slow) skinned muscle fibers at different temperatures [1]. To accurately replicate both shortening and
lengthening F-V, we simulated a three-state crossbridge model and optimized the 10 parameters to the data
obtained at 15°C. We then performed sensitivity analyses by varying each parameter individually ±50% of their
optimal value and calculated an error metric (EM), which was the normalized error from the experimental F-V
curves. We identified the three most important parameters and optimized those three parameters for all the data
at every temperature. We assessed the simulations by comparing our results to literature estimates of the state
transition rate constants and the population distribution in the different crossbridge states.
Results and Discussion: The sensitivity analysis showed three parameters produced the highest EM values (Fig.
1), which were the forward rate constants determining the transition between the three model states. The results
of optimization with those three parameters showed that the rate parameter estimates agreed with range of
literature estimates and the population of attached crossbridges to be 30.5%, in agreement with the 20-43% values
found in the literature. In conclusion, a three-state crossbridge with only three parameters is capable of predicting
both macroscopic F-V data and biophysical data.
Figure 1: Sensitivity analysis showing
1
the three most sensitive parameters to be
the forward rate constants between the
2
detached, pre-powerstroke, and postpowerstroke states. Upper plot: +50%
3
4
6
5
10
9
8
7
increase; Lower plot: - 50% decrease.
250
EM
200
150
100
50
0
250
1
EM
200
150
2
100
3
4
50
5
7
6
D
D
10
9
0
kmax,1
1
2
D
3
k0,2
kmax,2
Parameters
kmax,-2
E
1
E
2
8
E
3
References
1. Gollapudi and Lin (2014). Prediction of the In Vivo Force–Velocity Relationship of Slow Human Skeletal
Muscle from Measurements in Myofibers. Ann Biomed Eng. Vol. 41(8).
GAIT VARIABILITY IN INDIVIDUALS WITH HIP DYSPLASIA
Kari L. Loverro ([email protected]), MS, Anne Khuu, Eva M. Ciccodicola, and Cara L. Lewis, PhD
College of Health & Rehabilitation Sciences: Sargent College, Boston University, Boston, MA, USA
Hip dysplasia (HD) is characterized by decreased acetabular coverage of the femoral head, which can increase
stress leading to the development of pain and/or osteoarthritis [1]. Kinematic and kinetic gait changes in adults
with HD have been indicated as stress and pain reduction strategies [2]. However, to our knowledge, no studies
have investigated the variability of kinematic, as well as temporal-spatial measures in this population. Therefore,
the purpose of this study was to investigate gait variability in individuals with HD compared to healthy controls.
Sixteen individuals diagnosed with HD (14F, 2M; 26.2±8.5yrs; m 1.66±0.05m; m 67.26±8.7 kg) and 16 healthy
controls participated (14F, 2M; 25.7±6.6yrs; m 1.66±0.10m; m 62.83±9.5 kg). Kinematic data were collected
while participants walked for two minutes on a treadmill at two speeds: 1) their preferred (PRF) speed and 2) a
prescribed speed (PRSC: 1.25m/s). Visual3D was used to track kinematics and calculate temporal-spatial
parameters. Peak angles for the ankle, knee, hip, pelvis and trunk were extracted. With-in subject mean standard
deviation (meanSD) for each dependent variable was used to measure variability, at each speed. Independent ttests were used to compare between-subject meanSD differences at the PRF and PRSC walking speeds.
Preliminary analysis indicates that individuals with HD have significantly greater variability (meanSD) than
matched controls when walking at their preferred speed and when walking at a prescribed speed. These
differences were noted in double support time, step length and stride length at a preferred speed (p < 0.05), and
step length, stride length, and swing time when walking at a prescribed speed (p < 0.05). For the kinematic
measures, individuals with HD had significantly increased variability than controls in the sagittal and frontal
planes at the preferred and prescribed speeds (Table 1).
Individuals with HD had increased variability in gait kinematics and temporal-spatial measures when walking at
a preferred speed and when constrained to a specific speed. Although it has been speculated that adults with HD
use different kinematics and kinetics during walking to alleviate or compensate for pain, this is the first study to
suggest that increased variability may also be a factor.
Table 1: Selected significant kinematic results. Group mean (SD) of with-in subject meanSD.
Joint
Hip
Extension
Abduction
Group
PRF(o)
PRSC (o)
Control
Dysplasia
Control
Dysplasia
0.95(0.29)
1.17(0.29)
0.80(0.25)
0.92(0.31)
0.88(0.27)
1.22(0.55)
0.76(0.22)
1.00(0.30)
Joint
Pelvis
Anterior Tilt
Drop
Group
PRF(o)
PRSC (o)
Control
Dysplasia
Control
Dysplasia
0.77(0.18)
0.85(0.26)
0.65(0.26)
0.75(0.22)
0.72(0.16)
0.92(0.20)
0.55(0.10)
0.77(0.26)
Note: Bold indicates significant difference (p < 0.05) between groups
References
1. Murphy et al. (1995) The prognosis in untreated dysplasia of the hip. A study of radiographic factors that
predict the outcome. J Bone Joint Surg Am 77: 985-989.
2. Skalshøi et al. (2015) Walking patterns and hip contact forces in patients with hip dysplasia. Gait & Post 42:
529-533.
Acknowledgments
Research reported was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases
of the NIH under Award Numbers R21 AR061690 and K23 AR063235.
1,2,3
Improving post-stroke gait with a multi-joint implanted neuroprosthesis: a case report
Nathaniel S Makowski ([email protected]), 1,2Rudi Kobetic, 1,2Lisa M Lombardo, 1,2Kevin M
Foglyano, 1,2Gilles Pinault, 1,2,4Stephen M Selkirk, and 1,2,4Ronald J Triolo
1
Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
2
Advanced Platform Technology Center, Cleveland, OH, USA
3
Cleveland Functional Electrical Stimulation Center, Cleveland, OH, USA
4
Case Western Reserve University, Cleveland, OH, USA
Post-stroke gait is impaired by compromised volitional joint control, impaired muscle recruitment, and
hypertonia limiting function at the hip, knee, and ankle. About one third of stroke patients retain gait deficits
after physical therapy and may benefit from gait assistance. Patients with mild impairments may benefit from a
peroneal nerve stimulator or ankle foot orthosis. However, patients with more severe deficits require additional
assistance. This case report presents the therapeutic and neuroprosthetic effects of a fully implanted pulse
generator (IPG) for multi-joint assistance for walking after stroke.
The participant was a 64 year old male with left hemiparesis resulting
from a hemorrhagic stroke two years prior to participating in the study.
His gait was limited by impaired coordination, weakness, and mild
hypertonia. An 8-channel IPG and intramuscular electrodes were
implanted (Figure 1). The following muscles were targeted: tensor
fasciae latae, sartorius, gluteus maximus, short of biceps femoris,
quadriceps, gastrocnemius, tibialis anterior, and peroneus longus. After
implantation, a stimulation pattern was developed to assist with hip, knee,
and ankle movement. A heel switch in the sole of the shoe on the affected
side was used as a trigger to coordinate swing and stance phases of
stimulation with gait. The participant used the stimulator at home for
exercise and in the laboratory for stimulation assisted gait training.
Outcome measures include the 10m walk to assess gait speed and spatiotemporal parameters to evaluate contributions to changes in gait speed.
The participant was assessed under three conditions: 1) volitional walking Figure 1: Illustration of multi-joint
at baseline, 2) volitional walking after training, and 3) walking with implanted neuroprosthesis and the
stimulation after training. Comparisons include evaluating the
external control unit in use
1) therapeutic effect (baseline volitional vs. volitional after training),
2) neuroprosthetic effect (volitional after training vs. stimulation after training), and 3) total effect (baseline
volitional vs. stimulation after training).
The participant’s walking improved after gait training, both with and without the addition of electrical
stimulation. Therapeutic effects from training and exercise increased walking speed from 0.29m/s to
0.35m/s(p<0.05) while neuroprosthetic effects, superimposing electrical stimuli in coordination with volitional
gait, increased speed from 0.35m/s to 0.72m/s(p<0.05). Most of the spatio-temporal parameters improved,
showing more symmetric and dynamic gait. These results provide proof of concept that multi-joint electrical
stimulation coordinated with volitional effort can significantly improve post-stroke gait.
Acknowledgments
This work was supported by Merit Review Award No. B7692R from the United States Department of Veterans
Affairs Rehabilitation Research and Development Service. RJ Triolo was supported by Award No. A9259-L
from the Department of Veterans Affairs Rehabilitation Research and Development Service. NS Makowski was
supported in part by NIH Award No. U01 NS086872-01.
Towards More Efficient Robotic Training: Mixed Robotic Strategies
Laura Marchal-Crespo ([email protected]) and 1,2Robert Riener
1
ETH Zurich, Switzerland
2
Balgrist University Hospital, University of Zurich, Switzerland
1,2
Robotic guidance is often used to reduce performance errors while training motor tasks. However, research on
motor learning has emphasized that movement errors drive motor adaptation. Thereby, robotic algorithms that
augment movement errors have been proposed. Previous results suggest that haptic guidance enhances the
learning of timing components of motor tasks, whereas error amplification is better for learning the spatial
components [1]. Haptic guidance also seems to be particularly helpful for initially less skilled subjects, while
error amplification was found to be more beneficial for skilled participants [2]. Here, we present two examples
of mixed robotic strategies – i.e. training approaches that use two controllers in parallel that reduce or augment
errors depending on actual errors, or based on the timing and spatial characteristics of the task to be learned.
We developed a novel control algorithm that modulates movement errors by limiting dangerous and
discouraging large errors with haptic guidance, while augmenting awareness of task relevant errors by means of
error amplification. We also designed an algorithm that applies random disturbance torques that can work on
top of the error-modulating controller. The combination of the random disturbance and error-modulating
controllers increased of the kinematic errors and movement variability due to the error amplification and
random disturbance controllers, respectively, while limited large errors as a result of the haptic guidance.
We also developed a novel mixed guidance controller that combines haptic guidance and error amplification in
order to benefit learning of the timing and spatial components. A force field around the moving desired position
with a stable manifold tangential to the trajectory provides haptic guidance in velocity related aspects, and the
unstable manifold perpendicular to the trajectory amplifies the normal (spatial) error (Fig.1 Right). We
evaluated the mixed guidance controller with 29 healthy subjects using ARMin (Fig.1 Left) and found that
training with mixed guidance enhanced learning of the timing components when learning to track a line, but
limited learning when tracking a circle, probably because the guiding forces were too difficult to interpret.
Figure 1: Left ARMin IV is a 7 DoF
robotic device developed at ETH
Zurich for upper limb rehabilitation.
Right: Example of force field
generated by the second mixed
guidance controller at a desired
position on the target trajectory.
Up to date, robotic training strategies, developed in order to “fit all”, resulted in limited learning gains after
training. We hypothesize that the presented mixed strategies would provide an excellent framework to enhance
motor learning and neurorehabilitation. We will perform further experiments with healthy subjects and
neurologic patients in order to test our hypothesis.
References
1. Heuer H (2015) Robot assistance of motor learning: A neuro-cognitive perspective. Neurosci Biobehav Rev
56:222–240.
2. Milot MH (2010) Comparison of error amplification and haptic guidance training techniques for learning of
a timing-based motor task by healthy individuals. Exp Brain Res 201(2):119–31.
Acknowledgments
This work was supported by the Swiss National Science Foundation (SNF) through the grant number
PMPDP2_151319 and the National Centre of Competence in Research (NCCR) Robotics.
Gait Adaptability and Stability during Perturbed Walking in Young, Middle-Aged and Older Adults
Christopher McCrum ([email protected]), 2Gaspar Epro, 1Kenneth Meijer, 2Wiebren
Zijlstra, 2Gert-Peter Brüggemann, 2Kiros Karamanidis
1
Maastricht University, Maastricht, The Netherlands
2
German Sport University Cologne, Cologne, Germany
1,2
Gait stability declines and falls incidence increases with age [1, 2] and therefore, it is important to determine how
gait adaptability is affected across the adult lifespan. We aimed to examine gait stability and adaptation in young,
middle and older-aged adults in response to a sustained resistance gait perturbation, to test the hypothesis that
older adults can adapt their locomotion to gait perturbations, but not to the same extent as younger adults.
11 young (mean and SD: 25.5(2.1) years), 11 middle-aged (50.6(6.4) years) and 14 older (69.0(4.7) years) women
walked on a treadmill at 1.4m/s. After 10 minutes familiarization, an ankle strap was attached to the right leg and
participants walked for a further four minutes. Six consecutive steps from the end of this period were used to
determine a baseline. A 2.1kg resistance perturbation was then applied for one swing phase and removed by a
brake-and-release system via the ankle strap. Following a two minute washout period, the resistance was applied
for 18 consecutive steps of the right leg, followed by a final step of the right leg with the resistance removed.
Aftereffects were analyzed in the base of support (BoS). The margin of stability (MoS; difference between BoS
anterior boundary and extrapolated center of mass) was calculated at foot touchdown for all perturbed steps.
No significant age group differences were found during baseline and during the single perturbation period, with
all groups demonstrated significantly lower MoS in the single perturbation period in comparison to baseline
(p<0.05; Fig. 1). The older age group demonstrated significantly lower MoS for the first six steps of the sustained
perturbation period (p<0.05) compared with the young and middle-aged adults, however, there were no significant
differences between the groups for the last five steps (steps 14-18; p>0.05; Fig. 1). After removing the resistance,
all three age groups showed similar aftereffects (i.e. increased BoS).
Figure 1: MoS at foot touchdown (mean and SE) during nonperturbed walking (Base) and during the single and sustained
perturbation periods while walking on the treadmill at 1.4m/s.
All single and sustained perturbation period values were
significantly lower than baseline for all groups (p<0.05).
#: Significant difference between the older group and the
young and middle-aged groups in the first six steps (p<0.05),
with no difference between the young and middle-aged
groups.
In conclusion, our results provide evidence that with aging, the ability to recalibrate locomotor commands to
control stability is preserved. However, this recalibration may be slower in old age, which may have implications
for training interventions and falls prevention.
References
1. Süptitz F, Catala MM, Brüggemann GP, Karamanidis K (2013) Dynamic stability control during perturbed
walking can be assessed by a reduced kinematic model across the adult female lifespan. Hum Mov Sci. 32:
1404-1414.
2. Talbot LA, Musiol RJ, Witham EK, Metter EJ (2005) Falls in young, middle-aged and older community
dwelling adults: perceived cause, environmental factors and injury. BMC Public Health. 5: 86.
Preferred Walking Speed Selection in Normal and Unstable Gait Environments
1,2
Kirsty McDonald, 1 Peter Peeling, 1,2 Jonas Rubenson
1
The University of Western Australia, Perth, Australia
2
The Pennsylvania State University, State College, USA
Classic locomotion experiments have led to an understanding of walking with optimization of energy cost often
being cited as the determining principle underpinning human gait selection [1,2]. However, the need to maintain
locomotor stability is less well studied. The current project aims to explore energetic cost-based hypotheses of
neuromuscular function in walking to embrace the adapted, goal-directed, locomotor behavior of healthy adults.
Healthy young adults (n=21: 10m/11f; age: 27.4(±5.7) years) completed six randomized treadmill walking trials
(0.6, 0.9, 1.2, 1.5, 1.8m·s-1 and self-selected preferred walking speed; PWS) in normal footwear and custommade uneven footwear (one normal shoe, and one uneven ‘unstable’-shoe (UF) with an additional foam sole
attachment equal to ~10.5(±0.5)% of the participant’s lower limb length). At the conclusion of the UF
condition, the UF PWS was retested in subset of 13 participants. Metabolic consumption data was collected via
a portable Cosmed K5 device (Rome, Italy) with ~5min trial durations. For statistical analyses, a series of
repeated measures t-tests and ANOVAs were conducted with Bonferroni post hoc tests where appropriate. The
p value for statistical analyses was set at p < 0.05.
The normal footwear condition produced a group average PWS of 1.31(±0.16)m·s-1. When a second order
polynomial was fitted to the data (Fig 1), the minimum energetic cost was not significantly different from the
PWS. The initial self-selected UF PWS was 1.04(±0.13)m·s-1 and after completion of the UF condition
(~30mins total walking), participants selected a significantly greater UF PWS of 1.21(±0.19)m·s-1 (p = 0.005).
COT experimental data was not collected at the final UF PWS, however, the predicted COT (polynomial) was
not significantly different from the initial PWS COT. Interestingly, both the initial and predicted final PWS
COTs were significantly different from the minimum COT (p < 0.001 and p = 0.003, respectively) suggesting a
lack of energetic optimization, irrespective of familiarization level. Alternatively, we propose that participants
optimized gait stability in response to the
perturbation they were experiencing.
In a stable laboratory environment, COT is
minimized for humans walking at a range of
intermediate velocities, within which the selfselected PWS exists. However, it remains
unclear if stability optimization also contributes
to PWS selection. We are in the process of
investigating the alternative stability hypothesis
using three-dimensional motion analysis data
collected concurrently with the metabolic data
presented in the current study.
References
1. Ralston HJ (1958) Energy-speed relation and
optimal speed during level walking. Int. Z.
angew. Physiol. Einschl. Arbeitsphysiol
17:277-283.
2. Selinger JC, et al (2015) Humans can
Figure 1: Metabolic cost of transport vs. speed data for the
continuously optimize energetic cost during
normal and uneven footwear conditions. Preferred walking
walking. Current Biology 25:R795-R797.
speeds of each condition are indicated by a triangle. Second
Simple muscle-tendon model predicts positive force feedback leads to safer, but not faster perturbation
response during bouncing gaits
1
Michael McKnight, 1Shreyas Narsipur, 2Gregory S. Sawicki ([email protected])
1
Department of Electrical and Computer Engineering and 2Joint Department of Biomedical Engineering
1,2
North Carolina State University and 2UNC- Chapel Hill, Raleigh, NC, USA
Locomotion in the ‘real-world’ is often unsteady, as animals must maintain stability over uneven terrain. Muscular
response to perturbations can come from adaptations to feedforward motor drive, altered stiffness due to spinal
reflexes or even intrinsic, fast-acting ‘pre-flexes’ that arise from non-linear contractile properties of muscle [2].
Disentangling the relative contributions of these mechanisms for recovery has proven difficult in freely moving
animals, but a recent study [2] used a muscle-driven hopping model to demonstrate that combining feedforward
control (FF) and positive force feedback (PFF) reflexes (e.g., from Ib afferents) can improve stability.
Figure 1. (Left) Contour shows # of hopping cycles for lumped ankle plantarflexor to absorb excess mechanical energy due to
substrate height change equal to 40% of the resting MTU length. Note a region of invariant settling times (dark blue) at ~3 cycles
that includes both FF and FF+PFB strategies. (Right) Operating point of the muscle on its FL and FV curve for FF only and
FF+PFF strategies (indicated by dots on contour). MTU absorbs energy using shorter muscle lengths in FF +PFF case.
Here, we used a previously developed model of the human ankle plantarflexor MTU [1] to examine the muscle
activation parameter space trading-off the contribution from FF drive versus PFF gain in response to a change in
substrate height during simulated vertical hopping. Our model differed from [2] because it accounts for the
dynamics of series elastic tissues which serve to decouple the dynamics of the muscle from its load [1], a factor
that may play an integral role in shaping the perturbation response of a MTU and the limb as a whole.
Contrary to expectations based on [2], our results indicated that PFF does not improve the settling time in response
to perturbations compared to an open loop FF motor control strategy (~3 cycles in either case). Instead, PFF
serves to keep the muscle safe, allowing it to operate at shorter lengths over the course of recovery to steady state
hopping (Fig 1.). This result highlights the importance of considering muscle dynamics within the context of
series elastic structures when examining unsteady locomotion neuromechanics. Future work will seek to test these
model predictions in real MTUs during unconstrained work loop experiments [3] using a decerebrate preparation
with intact autogenic reflexes [4].
References
1.
2.
3.
4.
Robertson BD, Sawicki GS. (2014) J Theor Biol. 353:121-32.
Haeufle DF, Grimmer S, Kalveram KT, Seyfarth A (2012) J R Soc Interface. 9(72):1458-1469.
Robertson BD, Sawicki GS (2015) Proc Natl Acad Sci USA. 112(43): E5891-8.
Houk JC, Nichols TR (1973) Science. 181 (4095): 182-84.
Characterization of Human Motor Task Performance: Upper Limb Interaction with Circular Impedance
1
David Mercado ([email protected]), 1Brian Wilcox, and 1,2Neville Hogan
1
Dept. of Mechanical Engineering, MIT, Cambridge, MA, USA
2
Dept. of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
This study aims to characterize the human performance of complex interactive tasks; of present interest is the
rotation of a crank, or planar circular constrained motion. This motion can be defined by a single coordinate and
requires the participation of all shoulder and elbow muscles, a condition that will be exploited in future work.
Initial experimental findings of this task reveal velocity profiles of cyclical structure, repeatability across
multiple revolutions, and a sharp contrast between performance at high and low speeds.
The equipment used is the MIT-Manus, a robotic arm developed at the Newman Lab that has proven successful
at aiding neurologic disorder patients regain control of their upper limb reaching motions. [1] This device is a
planar motion robot arm composed of two links with direct motor drive and a handle as an end effector.
Through position feedback from the motor encoders and impedance control, the planar robot can constrain its
motion to a circular trajectory by applying forces proportional to the distance normal to the desired trajectory. A
high stiffness parameter effectively yields a virtual implementation of a circular crank.
During trials, 10 healthy subjects (both sexes, age 21-40) are asked to perform virtual crank rotations at a
preferred velocity, 2 rev/s, 0.5 rev/s, and 0.075 rev/s. Visual feedback of current velocity is provided. In Figure
1, it is apparent that the velocity trajectory of circular motion is cyclical with respect to position, suggesting the
possibility of rhythmic action primitives. Of interest is the apparent smoothness at higher velocities and the
presence of more discrete and less predictable motions at lower velocities, an observation previously made in a
similar experiment using a real crank. [2] Though an initial step, we expect this study will ultimately lead to a
heightened understanding of human motion control beyond reaching motions, advancements in upper limb
rehabilitation, and improvements in the control of prosthetics and manually operated devices.
Figure 1: Rotational velocity (mean & SD) of human performance of circular constrained motion vs. angular
position. Fast rotations (left) appear to be smoother and more repeatable than slow rotations (right).
References
1. Volpe, B.T., Krebs, H.I., Hogan, N., Edelstein, O.L., Diels, C. and Aisen, M., "A Novel Approach to Stroke
Rehabilitation Robot-Assisted Sensori-motor Stimulation," Neurology, 54 1938-44, 2000
2. Doeringer, J. "An Investigation of the Discrete Nature of Human Arm Movements." PhD thesis, MIT, 1999.
Acknowledgments
Funded by NIH, AHA, NSF, Eric P. and Evelyn Newman Fund, Gloria Blake Fund, and Lemelson Foundation.
VR effects spatial variability for each leg differently when learning a gait coordination task after stroke
1
Mukul Mukherjee ([email protected]), 1Troy Rand, 1Jessica Fujan-Hansen, 2Pierre Fayad
1
Dept. of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA
2
Dept. of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
Introduction: Perception of self-motion through Virtual Reality (VR) provides a unique avenue to improve gait
adaptation in chronic stroke survivors. Stroke subjects show deterioration in bilateral coordination during gait
[1]. Such deterioration may benefit from training to walk under different task constraints for each leg.
Moreover, variability of gait patterns shows characteristic shifts when health is compromised [2]. In stroke,
where one limb is more affected than the other, such shifts can reduce the impact of adaptive training.
Methods: In this ongoing study, chronic stroke survivors (n=11; 53.27±14.19 years, 8 males) walked on an
instrumented split-belt treadmill in either a VR (Figure 1) or a non-VR environment while being exposed to
different belt speeds for each leg. The affected leg was on the fast/slow belt if its stride length was
shorter/longer than the less affected side respectively. Reflective markers attached at specific landmarks were
tracked by motion capture cameras. Specifically, effects on variability (coefficient of variation – COV) of the
spatial variables - limb excursion and step length, were analyzed for each leg across the adaptation trials.
Results: Larger adaptive changes and lower variability was observed in the VR group (figure 1). While the VR
group showed a rapid rise and attainment of a stable state, the Non VR group showed a slower change that did
not plateau even at the end of the first split-belt trial (figure 1 inset). Mixed factorial ANOVAs showed
significant effects of the leg (fast/slow) across the adaptation trials for step length (p=0.003) and limb excursion
(p=0.029).
Post-hoc
analyses
Fast - Slow
Fast - Slow
showed
that
while
variability
for the
VR
Non VR
faster leg was not affected by VR,
the slower leg in the VR group
became less variable.
0.6
Limb Excursion (fast - slow)
0.4
Fig 1: A stroke survivor performing
the task (right bottom). Stride to
stride average of limb excursion in
VR (red) and non-VR group (black)
across the trials. Inset: the changes
in the first split-belt condition. The
light lines separate the first and the
last 10 strides for each trial.
0.2
0.0
-0.2
-0.4
Preferred
Walking 1
Slow
Walking 1
Fast
Walking
Slow
Walking 2
Split 1
Split 2
Catch
Split 3
Preferred
Walking 2
Discussion: Although VR may lead
to faster and enhanced adaptation after stroke, such changes may be impacted by different task constraints for
each leg during coordination training. The slower leg may more effectively utilize VR than the faster leg. This
maybe because the slower leg spends longer in stance and can respond to external feedback more accurately.
Appropriately assessing these differences may be the key to accelerated rehabilitation after stroke.
References
1. Hsu AL, et al. (2003) Arch Phys Med Rehabil. 2003; 84:1185-1193.
2. Balasubramanian CK et al. (2009) Gait Posture. 2009 Apr;29(3):408-14.
Acknowledgments
Supported by NIGMS/NIH Center of Biomedical Research Excellence grant (1P20GM109090-01)
Characteristic force intersection points present in standing balance coordination
Kieran Nichols ([email protected]), Wendy Boehm, Kreg Gruben
Human standing is mechanically complex and inherently unstable despite the ease with which most humans
perform the task. To maintain an upright posture, the neural system has to satisfy the laws of mechanics to
ensure control of translational and rotational motion. The force of the ground on each foot (F) is the output of
neuro-muscular coordination and drives whole-body motion, motivating the measurement of its direction (θF)
and location of application (center of pressure, CP). The purpose of this research was to identify simple linear
muscle coordination strategies to maintain upright posture in humans by identifying characteristic intersection
points (IP) of the F lines-of-action present in quiet human standing.
A linear relation between CP and tan(θF) may provide righting or tipping torque about the whole-body center of
mass (CM), depending on the resulting IP location relative to the CM. For example, a mechanical model in the
sagittal plane shows that F lines-of-action during standing have an IP when CP is modulated only with ankle
torque.1 That IP is 1) above the CM when the hip and knee are kept rigid (righting) and 2) near the knee when
hip and knee torques are kept constant (tipping). Humans are not constrained to use a linear CP vs tan(θF)
relation, but it could be a simple strategy if executed favorably (righting torque due to IP above CM). To
investigate the presence of linear coordination in non-disables humans, F was measured during quiet standing.
A line captured most of the CP vs tan(θF) variance (variance
accounted for: 88% in the 2-7Hz band, 77% for >7Hz). The IP
height was near the CM (just above the hip) for the 2-7Hz band
and near or below the knee joint for the >7Hz range (Fig. 1).
The strong linearity suggests a simple but precise coordination
strategy of hip, knee, and ankle torques.1 An IP in the 2-7Hz band
near the CM produces the F needed to accelerate the CM back
toward a central location without causing much torque that would
induce angular motion of the body as a whole.
p < 0.00001
IP height (fraction of hip height)
Six (4 female, age 20−53yrs) participants stood quietly with a
custom 6-axis force platform under each foot. F was recorded at
100Hz for 15s. Signals were filtered with either a 2nd order zerolag Butterworth filter of 2-7Hz band-pass or 7Hz high-pass. The
principal component of the CP vs tan(θF) relationship determined
the height of the IP, which was expressed as fraction of hip height.
1
0
2-7 Hz band
left
right
>7 Hz
left
right
Figure 1: F during human standing is
directed at a point (IP) located near the
height of the CM in the 2-7Hz band and
near the knee in the >7 Hz frequency
range.
In addition, the finding of an IP located near knee height for the
higher frequency (>7Hz) modulations is consistent with ankle
torque being modulated independent of hip and knee torques.1
These findings provide insight on the subtle muscle coordination
that may be essential for human upright posture and may be modified in those with postural difficulties.
References
Acknowledgments
ACT Hand: Exploring the Importance of Anatomical Structure in Human Hand Dexterity
Taylor D. Niehues ([email protected]) and Ashish D. Deshpande
While we admire and often attempt to replicate human hand’s versatile dexterity, we still don’t fully understand
how the hand’s anatomical structure, biomechanical properties, and neuromuscular controls contribute to hand
performance. A Greater understanding of hand functionality can lead to improved outcomes from surgical
procedures, more informed rehabilitation practices, and development of prosthetic and robotic hands with more
human-like robust manipulation capabilities. In this abstract we present an example of our methodology for a
better understanding of hand biomechanics and for examining its role in achieving hand dexterity.
The Anatomically Correct Testbed (ACT) hand is a robotic system that is designed to serve as a physical
simulation platform for examining the underlying mechanisms for human hand dexterity [1]. The joint kinematics,
bone structure, and muscle-tendon routing of the ACT hand closely mimic human musculoskeletal structure and
accurately reproduce the functional roles of hand muscles. Development of the ACT hand has been motivated by
the inherent limitations that exist in cadaveric, in vivo, and computer simulation studies. A physical simulation
offers specific advantages over these existing methods, including the ability to accurately simulate physical
interactions, and thus represents a valuable tool for studying hand biomechanics and control.
Understanding and accurately reproducing the hand’s mechanical structure is a challenging problem. Data from
cadaveric and in-vivo human studies are often unreliable for incorporating into the biomechanical models. Results
with cadaver and in-vivo approaches often have high variance due to inter-subject anatomic variability, sensitivity
of output data to experimental procedures, and uncaptured nonlinear effects. For example, if a thumb model
incorporates existing experimental human muscle moment arm data, it will not accurately replicate experimental
thumbtip forces.
We are using the ACT hand to examine how muscles
generate thumbtip forces, which are crucial for thumb
dexterity. Dynamic physical interactions, e.g. the
compliant connection between the thumbtip and force
sensor during cadaveric experimentation, are
inherently included in the ACT hand. These effects are
difficult to accurately model in simulation, which
ACT thumb, dorsal view
ACT thumb, palmar view
could contribute to discrepancies between simulation
and experimental results. Through iterative Figure 1: Tendon structure of the ACT thumb, and the thumbtip
forces produced by each muscle. Force produced by each muscleexperimental testing and re-design stages, we closely tendon unit matches well with the data collected on human subjects.
matched human thumbtip force data, with the ACT
thumb, while retaining anatomically accurate tendon origins, routing, and insertion points (Fig. 1). Analysis of
human and robot data and subsequent design changes resulted in a physical system that represents a normative
thumb model and accurately mimics human hand functionality, demonstrating the power of using the ACT Hand
as a physical simulation platform.
Our current work is focused on replicating human-like control in the ACT hand, including implementation of
biomechanical muscle models, various neuromuscular control theories, experimental paradigms to push the
boundaries of our understanding of the inner workings of our hands.
References
1.
Deshpande, AD et al (2013) Mechanisms of the Anatomically Correct Testbed Hand. IEEE/ASME
Transactions on Mechatronics 18:238-250.
Acknowledgments
Supported, in part, by the National Science Foundation (grant # IIS-1157954).
A Neuromuscular Algorithm for a Powered Foot-Ankle Prosthesis Shows Robust Control of Level
Walking and Stair Ascent
Nishikawa K1 ([email protected]), Davis K2, Han Z3, Hessel A1, Lockwood E2, Petak J2, Tahir U1,
and Tester J2
1
Center for Bioengineering Innovation, Northern Arizona University, Flagstaff, AZ, USA
2
Department of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ, USA
3
BionX, Inc. Bedford, MA, USA ([email protected])
The BiOMTM is a powered, foot-ankle prosthesis for persons with trans-tibial amputation [2].
Provision of motor power permits faster walking than passive devices. However, use of active motor
power raises the issue of control [1]. The control approach exhibits no inherent adaptation to varying
environmental conditions. Instead, algorithms generate positive feedback torque control for all intended activities and variations of terrain. Although the BiOM performs well across a range of level
and ramp walking speeds, more robust control algorithms could improve users’ experience for allterrain walking. Bio-inspired algorithms may offer that robustness. Current drawbacks of neuromuscular controllers lie in the use of Hill-type muscle models, which lack the ability to predict history
dependent muscle properties. These properties enable muscles to adapt instantaneously to changes in
load without requiring sensory feedback [3]. We recently developed a “winding filament” hypothesis
for muscle contraction that incorporates a role for the giant titin protein in active muscle [3]. The
winding filament hypothesis accurately predicts intrinsic muscle properties [3]. This new hypothesis
could allow us to develop robust control algorithms for control of powered prostheses. Our goal was
to develop a WFH-based control algorithm for the BiOM prosthesis and test its function during level
walking, stair ascent and descent, and backwards walking. The control algorithm incorporates a pair
of virtual muscles that emulate the subject’s shank muscles: an anterior muscle for dorsiflexion and a
posterior muscle for plantarflexion. The force produced by each virtual muscle is calculated using a
model inspired by the winding filament hypothesis. In each time step, the simulation calculates the
length of the anterior and posterior muscles based on ankle angular position. The length of each
muscle is determined from a sensor on the prosthesis that measures the ankle angle. The model estimates the torque produced by each muscle based on its length and level of activation. The muscles
are activated in a simple pattern: the dorsiflexor is activated at ~50% during swing, and the plantarflexor is activated at ~50% during stance. The control algorithm calculates the net ankle torque at
each time step. We tested four male subjects during level walking and stair ascent. Our results show
that the WFH-based control algorithm for the BiOM prosthesis produces ankle torque profiles during
level walking that are similar to the BiOM stock controller and human ankle [2]. The WFH-based
control algorithm also reproducing ankle torque profiles that match those of able-bodied individuals
during stair ascent [4] with minimal sensing (i.e., ankle angle) and no change in activation or model
parameters. Our research demonstrates successful implementation of a neuromuscular controller for
a powered foot-ankle prosthesis based on the winding filament hypothesis. By adapting instantaneously to changes in load, our control algorithm achieves more robust prosthesis control.
References
1. Farrell MT, Herr H (2011) A method to determine the optimal features for control of a powered
lower-limb prostheses. Conf Proc IEEE Eng Med Biol Soc 2011:6041-6.
2. Herr HM, Grabowski AM (2011) Bionic ankle-foot prosthesis normalizes walking gait for persons
with leg amputation. Proc R Soc B 279:457-64.
3. Nishikawa KC, Monroy JA, Uyeno TA, Yeo SH, Pai DK, Lindstedt SL (2012) Is titin a winding
filament? A new twist on muscle contraction. Proc Roy Soc B 297:981-90.
4. Sinitski EH, Hansen AH, Wilken JM (2012) Biomechanics of the ankle-foot system during stair
ambulation: implications for design of advanced ankle-foot prostheses. J Biomech 45:588-94.
Acknowledgements
Supported by NSF IOS-0742483, IOS-1025806, IIP-1237878, IOS-1456868 and IIP-1521231.
1
EEG Motion Artifact Assessment and Attenuation
Andrew D. Nordin ([email protected]) and Daniel P. Ferris
Motion artifacts captured by scalp electroencephalography (EEG) present a significant barrier to understanding
human brain dynamics during movement [1, 2]. Our long term goal is to devise hardware and software
innovations to reduce or completely remove motion artifact from scalp EEG recordings. As a step towards that
goal, we created an electronic phantom head for recording scalp EEG during motion and comparing electrode
data to ground truth signals. We constructed a phantom human head using dental plaster and 8 embedded
dipolar sources [2] to generate eight contrasting artificial neural signals (randomly occurring 500 ms sinusoidal
bursts). We assembled a dual electrode array using ActiveTwo hardware (BSM, BioSemi), including 8 scalp
electrodes recording normal EEG and 8 inverted, rigidly coupled noise electrodes recording only motion artifact
without brain signals. The electrically isolated noise electrodes were referenced to an overlaid custom
conductive secondary cap. Using a robotic motion platform, we moved the phantom head through sinusoidal
head motions [2]. Motion conditions included Stationary, 1.00, 1.25, 1.50, 1.75, and 2.00 Hz frequencies (4 cm
amplitude, 5-minute duration) [2]. We evaluated EEG data quality relative to the ground truth signals under
three setup conditions: standard scalp EEG preparation (Standard), securing scalp EEG electrodes and wires
with a secondary cap (EEG + Cap), and spectral subtraction of electrically isolated noise electrodes from EEG
electrodes (Subtracted) [1]. We hypothesized that motion artifact would decrease scalp EEG signal quality, and
that EEG signal quality could be improved by securing electrodes and wires, and by subtracting the noise
signals from the EEG signals. We computed SNR (20log10(EEG/Noise)) from the root mean square of the
respective EEG and Noise signals. We analyzed SNR and cross correlation among conditions with separate 3 x
6 (setup x motion condition) repeated measures ANOVAs (α = 0.05). Our results show SNR and crosscorrelation decreased during motion using a standard scalp EEG preparation (Figure 1). Securing electrodes and
wires with a secondary cap reduced motion artifact, providing an electrical reference for noise electrodes, which
further reduced motion artifact after spectral subtraction (Figure 1). Applying these methods in human
movement research should improve real-world neuroimaging with EEG.
12
Cross Correlation (r)
11
SNR (dB)
1.00
2.00Hz: Subtracted > EEG + Cap & Standard
10
9
8
2.00Hz: Subtracted > EEG + Cap > Standard
0.95
0.90
0.85
0.80
0.75
0.70
Stationary
1.00Hz
1.25Hz
1.50Hz
1.75Hz
2.00Hz
Standard:
Stationary & 1.00Hz & 1.25Hz & 1.50Hz & 1.75Hz > 2.00Hz
EEG + Cap: 1.00Hz & 1.25Hz > Stationary & 1.50Hz & 1.75Hz > 2.00Hz
Subtracted: 1.00Hz > Stationary & 1.25Hz & 1.50Hz & 1.75Hz & 2.00Hz
Stationary 1.00Hz
1.25Hz
1.50Hz
1.75Hz
2.00Hz
Standard
Stationary > 1.00Hz > 1.25Hz & 1.50Hz > 1.75Hz > 2.00Hz
EEG + Cap Stationary > 1.00Hz > 1.25Hz & 1.50Hz & 1.75Hz & 2.00Hz
Subtracted Stationary > 1.00Hz & 1.50Hz > 1.25Hz & 1.75Hz & 2.00Hz
Figure 1: Eight electrode mean (± standard error) in each motion condition. (Left) signal to noise ratio (SNR),
(Right) time series cross-correlation (r) between signals recorded while stationary and each motion condition.
References
1. Chowdhury ME, et al. (2014). Reference layer artefact subtraction (RLAS): a novel method of
minimizing EEG artefacts during simultaneous fMRI. Neuroimage. 84:307-19.
2. Oliveira A., et al. (In Review) Independent component analysis can attenuate motion artifact effects on
brain electrical activity recorded with electroencephalography during head movement. J of Neural Eng.
Acknowledgments
Supported by Cognition and Neuroergonomics Collaborative Technology Alliance ARL W91 1NF-10-2-0022.
Entrainment of Overground vs. Treadmill Human Walking to Mechanical Perturbations at the Ankle
1
Julieth Ochoa ([email protected]), 2Dagmar Sternad, and 3Neville Hogan
1
Massachusetts Institute of Technology, Cambridge, MA, USA. Masters Candidate.
2
Northeastern University, Boston, MA, USA. Professor.
3
Massachusetts Institute of Technology, Cambridge, MA, USA. Professor.
Unlike upper-extremity robotic rehabilitation, robotic therapy of lower extremities has not matched the
effectiveness of human-administered approaches. We hypothesize that this may stem from inadvertent
interference with natural movement control and investigated the oscillatory dynamics of human locomotion.
Specifically, we assessed gait entrainment to periodic mechanical perturbations. Because the treadmills used in
most studies necessarily interact with the dynamics of natural locomotion, we compared gait entrainment in
treadmill and overground walking.
Fourteen healthy subjects walked overground and on a treadmill while wearing an exoskeletal ankle robot
which exerted 50 consecutive short plantarflexion torque pulses at periods 50 ms faster or slower than the
subjects’ preferred stride period. In all trials, subjects performed a cognitive distractor task. The gait phase of
each perturbation was determined as the percentage of the gait cycle that coincided with the onset of the torque
pulse (starting from the 50th perturbation). A linear regression of gait phase onto perturbation number was
applied to the last 10 perturbations in each trial to assess entrainment as a zero-slope segment.
Overground
Perturbation Torque Phase (%)
400
300
200
100
50
0
-100
-200
-300
1
10
30
40
50
Treadmill
400
Perturbation Torque Phase (%)
20
300
200
100
50
0
-100
-200
-300
Entrainment to the periodic perturbation occurred in all
conditions, however more readily in overground walking
(Figure 1). If gait entrainment was a result of voluntary
synchronization, then the onset of phase convergence should
have occurred within the first few perturbation cycles. Instead,
a rather moderate-to-slow convergence was observed in
overground and treadmill trials, occupying 24 and 32
perturbation cycles on average respectively. To our
knowledge, this is the first study demonstrating dynamic
entrainment to external periodic plantarflexion perturbations at
the ankle joint during overground walking. We submit that
these results show clear, behavioral evidence that a nonlinear
neuro-mechanical oscillator with a limit-cycle plays a
significant role in human locomotion. Furthermore, in all
entrained trials the stride period phase-locked with the
perturbation pulse at ‘push-off’ such that it assisted propulsion.
Additionally, the entrained gait period often persisted after
perturbations were discontinued; again, this occurred more
readily overground than on a treadmill. This entrainment of the
stride period and its sensitivity to context indicate the subtlety
and adaptability of human walking. Our observations suggest
new avenues for gait rehabilitation and implications for
exoskeleton design and legged locomotion research.
Acknowledgments
The
authors
were
partially
funded by the following sources:
Perturbation Torque Number
NIH Grant HD045639, AHA 11SDG7270001, NSF DMSFigure. 1: Regression of perturbation torque phase 0928587, the Eric P. and Evelyn E. Newman Fund, the Gloria
vs. perturbation number for all entrained gaits.
Blake Fund, and a GEM Fellowship.
1
10
20
30
40
50
Understanding the Mechanisms of Scapulohumeral Rhythm with the HARMONY Exoskeleton
Evan M. Ogden ([email protected]) and Ashish D. Deshpande
The coordinated movement patterns between the humerus and the shoulder girdle (i.e., the clavicle and scapula)
is known as scapulohumeral rhythm (SHR). This coupling is critical to providing glenohumeral joint stability and
properly aligning the shoulder muscles. This synchronicity varies significantly among individuals and tasks with
different loading characteristics. The loss or alteration of this coordinated behavior has been associated with
various neuromuscular pathologies, including stroke and spinal cord injuries, and leads to impaired arm function
and an increased risk of shoulder impingement. Furthermore, it has been shown that patients with greater proximal
arm capabilities at the onset of rehabilitation have substantially improved recovery of hand function than those
with reduced shoulder mobility [1]. The ability to concurrently measure and control SHR is essential to
ascertaining what factors govern SHR and how they can manipulated to produce healthier motion.
To address these questions, we have developed an upper body exoskeleton called HARMONY (Fig. 1). The
kinematics of the robot closely match the physiological motions of the shoulder complex, including the
elevation/depression and protraction/retraction of the shoulder girdle [2]. Its compact design supports bilateral
arm movements and facilitates a wide range of motion that envelops the majority of activities of daily living. The
robot’s sensors and actuators allow it to simultaneously apply various loads to the upper limb and measure the
user’s response to these demands. We are currently conducting human subject studies with both healthy and stroke
subjects to evaluate the device’s capacity to alter the user’s SHR.
These features allow us to thoroughly explore multiple
aspects of shoulder biomechanics and control. The robot’s
ability to monitor and modify SHR when performing tasks
will help us discern how task performance depends on this
coordination. Observing how healthy subjects respond to
external disruptions to SHR can provide insight into
compensatory movement strategies and potential injury
mechanisms. In addition, pairing HARMONY with various
physiological sensors, such as EMG, can be used to study
how the shoulder complex responds to perturbations.
Finally, utilizing this exoskeleton as a rehabilitation device
will allow us to examine the relationships between SHR,
functional recovery after therapy, and shoulder
impingement. These findings have the potential to improve
rehabilitation techniques, enhance our understanding of
neuromuscular recovery, and accelerate patient recovery.
Figure 1: The HARMONY exoskeleton.
References
1. Houwink A et al. (2013) Functional recovery of the paretic upper limb after stroke: who regains hand
capacity? Arch Phys Med Rehab 94 (5):839–844.
2. Kim B and Deshpande AD (2016) An Upper-Body Rehabilitation Exoskeleton with an Anatomical Shoulder
Mechanism: Design, Modeling, Control, and Performance Evaluation. Manuscript submitted for publication.
Acknowledgments
This work is supported, in part, by the National Science Foundation (Grant # 1157954) and TIRR Foundation.
Explosive Torque Production in Knee Extensors & Plantar Flexors and Their
Relationship to Whole Body Response During Unexpected Perturbations
Matthew TG Pain ([email protected]), Fearghal Behan and Jonathan Folland
1
Loughborough University, Loughborough, Leicestershire, UK
Given the short duration of many tasks the ability to develop and control force rapidly can be more important
than maximal force. This study investigated rate of torque development (RTD) in the knee extensors and plantar
flexors of young healthy subjects and how this relates to their ability for whole body recovery from unexpected
perturbations when standing on one leg.
10 untrained males (24.6 ± 5.5 years, 1.81 ± 0.10 m, 81.9 ±10.4 kg) and 7 untrained females (23.3 ± 2.8 years,
1.69 ± 0.06 m, 63.2 ± 7.0 kg) gave informed consent to take part in the study. Different custom dynamometer
rigs for the knee extensors (KE) and the plantar flexors (PF) were used to record maximum voluntary isometric
torque (MVT), and maximum explosive voluntary torque (EVT) contractions from both legs independently.
EVT were used to determine RTD, with the average of 10 repetitions used per subject per leg. Perturbations
involving the subjects standing on one leg at a time were completed on a CAREN system. Kinematic data were
collected using a nine camera motion analysis system. Fifty-seven 14 mm spherical markers were used to
determine whole body centre of mass (COM). Only anterior platform displacements, 0.45 m.s-1 and 0.1 m, were
analyzed but were interspersed with random perturbations in other directions. Pearson’s correlation coefficients
were calculated to assess relationships, significance p < 0.05. EVT values were compared at 25, 50, 75, 100,
150 and 200 ms from torque onset (e.g. EVT25). The mean time for the COM acceleration to change direction
after perturbation was 300 ms and so COM accelerations at 200 250 and 300 ms were assessed.
Mean MVT of KE and PF were 247 ± 80.8 N.m and 234 ± 60.4 N.m. Significant correlations were found for
KE vs PF MVT for both absolute and normalized to body mass values (r = 0.832, r = 0.620). EVT values for
KE vs PF, absolute and normalized to body mass, were significantly correlated over the EVT50-EVT200 range
(r = 0.488 to 0.763), whereas when normalized to MVT the EVT25-EVT150 values were significant (r = 0.353
to 0.469). What is more interesting is the pattern of correlations that support a significant difference in the shape
of the RTD curves. During the earlier, more neurally determined explosive periods, the PF are slower to initially
develop torque, but then have greater late RTD compared to the KE. For COM acceleration at 300 ms post
perturbation there were significant correlations for KE EVT25 and EVT50, as well as PF EVT50 and EVT75
(Table 1). MVT for KE or PF were not correlated with COM accelerations. With the PF being slower than the
KE it is interesting that KE EVT25 and EVT50 were significant whereas for PF it was EVT50 and EVT75.
This study shows that different muscle groups in the same limb with the same MVT have different voluntary
RTD profiles under isolated maximal, volitional conditions and these differences are reflected in functional
responses to balance recovery from perturbations. It also shows that early neurally mediated RTD, which varies
by muscle group, is a key factor in reducing and then reversing COM acceleration after a perturbation in this
group of healthy young adults who would be expected to have similar reaction times and muscle-tendon health.
This could have implications for determining artificial stimulations levels and activation profiles during
modelling of different muscles when rate of force or torque development is critical as generic activation models,
or equal stimulation levels, would not produce realistic muscle by muscle outputs given the results here.
COMacc. 200
COMacc. 250
COMacc. 300
KE25
0.009
-0.110
-0.575*
KE50
0.016
-0.120
-0.562*
KE75
-0.077
-0.121
-0.465
KE100
-0.156
-0.121
-0.424
PF25
-0.076
-0.067
-0.440
PF50
-0.046
-0.072
-0.560*
PF75
-0.039
-0.068
-0.528*
PF100
-0.027
-0.062
-0.381
Table 1. Correlation coefficients of KE & PF torque (Nm) with COM acceleration (m.s-2) at 200-300 ms. * p < 0.05.
Acknowledgments Supported by ARTHRITIS RESEARCH UK.
Practice-induced Changes in Cortical Activity During Bimanual Skill Learning: An EEG Study
Se-Woong Park ([email protected]), Hannah Tam, and Dagmar Sternad
Understanding the changes in the spatiotemporal pattern of cortical activity
during skill learning is of utmost interest for both basic and applied questions.
However, sufficiently high temporal resolution can only be obtained using
magnetoencephalography (MEG) or electroencephalography (EEG). The more
widely available and convenient EEG has been problematic for larger-scale
movements due to multiple sources of noise. However, recent advances in
EEG artifact removal motivated this study that recorded EEG during a
bimanual task [1]. Extending from a previous study on learning an asymmetric
bimanual skill that showed increasing but limited individuation of the two
arms [2], we examined the change in cortical activity during practice of the
same asymmetric bimanual skill.
Discrete
Rhythmic
20
Symmetric
B
300
200
10
0
10
0
-10
100
2
4
6
Session
C
0
10
0
-10
8
0
10
Velocity (deg/s)
Perturbation (deg)
Asymmetric
Electric Potential (uV)
Eight healthy right-handed subjects were instructed to rotate their forearms in
the horizontal plane and move their right arm to a target cue as fast as
possible, without disturbing the continuous oscillations of the left arm.
Subjects performed 150 discrete movements triggered at random phases of the
ongoing oscillations in the left arm (Fig.1A). The task goal was to achieve
high peak velocity, while minimizing perturbations of the continuous rhythmic
movements. Subjects practiced for over 10 daily sessions. Cortical activity
during performance was measured using 64-channel EEG electrodes in the 1st,
6th and 10th practice session. For comparison, EEG was also recorded during
bimanual cued discrete movements. Prior to the analyses, noise and artifacts
were eliminated using the adaptive mixture independent component analysis
(AMICA). The event-related potentials (ERP) were aligned with the visual cue
onset with an epoch size of [-300, 500ms]. To quantify the difference between
the two conditions as practice progresses, we quantified the time where the
ERPs between the asymmetric and symmetric conditions differed.
A
Subject 1
Symmetric
Session 6
Asymmetric
T6 200
100
Session 10
T6<T10
0
100
200
T10
Time from Cue Onset (ms)
Figure 1
Behavioral results showed that the performance of both arms significantly improved: peak velocity of the cued
discrete movement increased and the perturbation of the rhythmic movement decreased, although not reaching
zero (Fig.1B). Focusing on the left motor area the signal at C3 differed between the symmetric and asymmetric
condition at ~200ms following cue onset (Fig.1C). This difference disappeared after 10 practice sessions.
These first results show that 1) EEG artifacts during bimanual upper-limb movements can be effectively
removed by AMICA, 2) cortical activity for discrete reaching depends on the movement of the opposite arm, 3)
this difference is reduced by practice, consistent with the individuation of the two arms. These findings suggest
that specific EEG features can characterize changes in neural activity across 10 days of practice. This study
presents a first step toward understanding practice-induced plasticity in the time domain, which may inform
brain-computer interfaces with non-invasive electrophysiology.
References
1. Gwin JT, Gramann K, Makeig S, and Ferris DP (2011) Electrocortical activity is coupled to gait cycle
phase during treadmill walking. Neuroimage 54:1289–96.
2. Park S-W, Ebert J, and Sternad D (Under Review) Plasticity of interhemispheric interference in an
asymmetric bimanual task.
1,2
Does TMS Perturb the Gait Cycle?
C. Patten ([email protected]), E.L. Topp, T.E. McGuirk, E.R. Walker, C.L. Banks, and V.L. Little
1
Neural Control of Movement Lab, Malcom Randall VAMC, Gainesville, FL, USA and
2
University of Florida, Gainesville, FL, USA
Understanding cortical control of human locomotion is of particular relevance to neuropathological conditions
affecting gait. Seminal studies conducted in healthy individuals used transcranial magnetic stimulation (TMS)
during walking. Such investigations remain limited, especially in patient populations. Importantly, peri- and
supra-threshold TMS transiently interrupt ongoing motor activity thus are potential perturbations to the nervous
system which could be exaggerated by neuropathology. It remains unclear whether TMS, delivered at intensities
required to elicit motor evoked responses, alters the walking pattern thus limiting its utility for investigation in
patient populations. Here we compared kinematics, kinetics, and EMG patterns between walking with and
without supra-threshold TMS. We found no significant deviation in gait patterns with supra-threshold TMS.
The primary challenge of using TMS during walking is to maintain coil position on a moving subject. We
developed a TMS positioning helmet that maintains coil targeting, eliminating the need to manually hold the
coil [1]. The helmet conforms to each subject’s head and a suspension supports the weight of the coil and its
cable (about the same as an adult head). Coil stabilization is effective during walking and targeting is repeatable
when the helmet is removed and later re-donned. In addition to position and angle accuracy, motor evoked
responses (MEPs) collected during treadmill walking while wearing the TMS helmet are stable [Figure 1].
We studied 14 chronic (>6 mos) stroke survivors (mean 63.7 yrs, LE FMA 29.8/34) and 10 healthy, agematched controls (mean 60.2 yrs) during walking at self-selected speed on an instrumented split-belt treadmill.
Single-pulse TMS (1.2x aMT) targeting ankle plantarflexors (PF) was triggered by acquisition of 8 known
biomechanical events distributed across the gait cycle. Stimulations were delivered every three to four steps in
fully randomized order of gait event.
Figure 1: Medial gastrocnemius (MG) MEPs elicited during walking.
Mean (±SEM) over multiple (>35) gait cycles in one individual.
TMS delivered at pre-swing reduced peak PF angle in initial swing by ~1
degree (p=.012). TMS delivered at terminal stance (p=.029), initial swing
(p=.003), and mid swing (p=.002) increased peak dorsiflexion angle
μ
during swing by ~2 degrees. Despite these small differences in sagittal
plane ankle angles, no significant effect of TMS was observed for ankle
PF power or the rate of ankle PF power production (p’s >.05).
!
"
Furthermore, no differences in EMG were detected between walking with
and without TMS (p’s >.05). No consistent effects were observed on any
aspect of the gait pattern in either stroke survivors or healthy controls
when supra-threshold TMS was applied during walking. Our results establish validity for use of TMS as an
assay of cortical control of locomotion in healthy adults and stroke survivors.
References
1. Topp, E.L., and Patten, C.: ‘Securing a TMS Coil to the Patient's Head’, U.S. Patent Application No.
14160584, 2014.
Acknowledgments
This work is supported by NIH-NINDS 1R21NS091686-01 and the Department of Veterans Affairs
Rehabilitation R&D Service (Research Career Scientist Award #F7823S and Merit Review Grant #N1677R).
1
Compression Garments Alter Sensory Transmission in the Upper Limb
Gregory Pearcey ([email protected]) 1Trevor Barss, 2Bridget Munro and 1E Paul Zehr
1
University of Victoria, Victoria, BC, Canada
2
NIKE Exploration Team, NIKE Inc., Beaverton, OR, USA
Cutaneous feedback from the skin provides perceptual information about joint position and movement.
When integrated with other sensory modalities, cutaneous feedback provides accurate measurements of position
and movement around joints. However, it is currently unknown whether constant tactile input to the skin may
alter excitability through changes in pre-synaptic inhibition of muscle afferent feedback. Thus, the purpose of
the current experiment was to examine if sustained input to the skin (compression garment) modulates sensory
feedback transmission in the upper limb.
On two separate days, university aged participants performed two parts of the experiment, each of which
was completed under two conditions; CONTROL (no cutaneous input), and COMPRESSION (compression
sleeve applied across the elbow joint). In both parts of the experiment, electromyography (EMG) flexor and
extensor carpi radialis was measured prior to and in response to stimulation of the median nerve just proximal to
the elbow to elicit H-reflexes in the flexor carpi radialis. In part 1, M-H recruitment curves were performed at
rest, during 10% wrist flexion, superficial radial nerve conditioning during 10% wrist flexion, and distal median
nerve conditioning during 10% wrist flexion. Cutaneous reflexes were elicited during 10% wrist flexion via
stimulation of the superficial radial and distal median nerves. In part 2, M-H recruitment curves were performed
at rest, during unloaded arm-cycling (1Hz) and during a discrete reaching task.
Results from both parts of the study suggest that constant tactile input to the skin via compression
garments modulates the excitability of afferent connections independent of descending input. This was
evidenced by a general suppression of the H-reflex, regardless of conditioning (see Figure 1) or task being
performed. Furthermore, increased long latency cutaneous reflex amplitudes occurred when a compression
sleeve is worn. Therefore, pre- or post-synaptic changes within a limb receiving constant cutaneous input may
alter the functional “set–point” of ongoing motor output. This is indicative of segmental changes in spinal reflex
excitability independent from descending input and changes to the muscle.
Figure 1: A: Single subject FCR average of 10 EMG traces comparing control (solid) to compression (dotted)
H-reflex amplitudes with different conditioning paradigms. B: Group average M-wave amplitudes during each
conditioning paradigm. C: Group average H-reflex amplitudes during each conditioning paradigm.
Acknowledgments
The authors would like to thank NSERC and the NIKE Sport Research Lab for support on this project.
Model-based Analysis of Condition-dependent Vestibular Contribution to Human Balance Control
1
Robert J. Peterka ([email protected]) and 2Adam D. Goodworth
1
Oregon Health & Science University, Portland, OR, USA
2
University of Hartford, West Hartford, CT, USA
Galvanic vestibular stimulation (GVS) provides a direct vestibular perturbation that can be used to investigate
the vestibular contribution to balance control. However, the artificial nature of GVS needs to be understood
when interpreting experimental results. We utilized a model-based interpretation of experimental body sway
responses to combinations of GVS and surface-tilt stimuli (STS) to identify how the vestibular contribution to
balance changes as a function of test conditions and how GVS differs from natural vestibular stimulation.
Frontal-plane body sway of 9 young adults (mean age 24.6 years, 5 female) was evoked in 9 different eyesclosed test conditions using pseudorandom STS, GVS, and simultaneous mathematically uncorrelated STS and
GVS with different amplitudes of GVS (0.75, 1.5, 3.0mA peak-peak) and STS (2° and 4° peak-to-peak) applied
on different tests. All tests were performed with eyes closed. The stimulus-evoked response was the center of
mass (CoM) sway angle with respect to vertical. Fourier analysis was applied to the stimulus and CoM sway
responses to compute frequency response functions (FRFs) that characterized the dynamic properties of balance
control. Parameters of a balance control model [1] were identified that accounted for experimental FRFs with a
focus on the measurement of proprioceptive and vestibular weight factors that represent the relative
contributions to balance control of information from these two sensory systems. Different model structures were
investigated to determine which accounted best for the unnatural vestibular stimulation provided by GVS.
FRFs from the various test conditions were compared to identify differences in sway dynamics between
responses to STS and GVS stimuli, stimulus amplitude-dependent changes, and interactions between
simultaneously presented STS and GVS. When GVS amplitude was increased while STS amplitude remained
constant, (i) the GVS FRF amplitudes decreased, indicating a decrease in vestibular weighting, and (ii) the STS
FRF amplitudes simultaneously increased, indicating an increase in proprioceptive weighting and thus
demonstrating a coupled reweighting of proprioceptive and vestibular contributions to balance. The overall
shapes of STS and GVS FRFs differed with GVS responses having a lower bandwidth. Modeling results
accounted for the different shapes of FRFs if we assumed that central vestibular processing provided both
angular velocity and angular position information but that GVS perturbed only the angular velocity contribution
to balance. Additionally, modeling results showed that the GVS-perturbed angular velocity contribution was
time delayed by 330 ms relative to the natural vestibular signal.
Our results indicated that GVS only influences vestibular angular velocity signals. This result is consistent with
GVS evoking changes in semicircular canal afferent signals such that the vector combination from all 6 canals
signals a net angular roll velocity [2]. Although otolith afferents are known to be sensitive to GVS, the wide
directional distribution of otolith hair cells results in no net GVS-evoked otolith angular position signal
contributing to balance control. Understanding how humans respond to artificial vestibular stimulation in a
variety of conditions is important for the future development of a vestibular prosthesis that uses electrical
stimulation to improve balance control.
References
1. Peterka RJ (2003) Simplifying the complexities of maintaining balance. IEEE Eng Med Biol Mag 22:63-68.
2. Fitzpatrick RC, Day BL (2004) Probing the human vestibular system with galvanic stimulation. J Appl
Physiol 96:2302-2316.
Acknowledgments
Supported by NIH R01 DC010779.
Physical & Cognitive Demand of Immersive Virtual Reality during Balance-beam Walking
1
Steven M Peterson ([email protected]) and 1Daniel P Ferris
1
Virtual reality has been increasingly used in research and rehabilitation because it provides robust and novel
real-world sensory variation in a controlled environment. However, this relies on successful immersion to
mimic reality. This study examines the physiological effects of complete visual immersion using a headmounted display. Twenty subjects (10 males and 10 females; ages 19-32) were tasked with a physically and
cognitively demanding task of walking on a 1.5 inch wide balance beam raised 1 inch off of the ground.
Subjects performed three separate conditions: a real world condition without virtual reality, a virtual condition
where the beam appeared to be low off the ground (virtual reality low), and a virtual condition where the beam
appeared to be high above the ground (virtual reality high). A previous study showed that subjects exposed to a
balcony 15 meters above the ground exhibited more cautious gait in relation to their perceived height off the
ground [1]. This indicates that perception of high heights can affect motor performance. We hypothesized that
(1) virtual reality would result in increased cognitive load compared to real world, indicated by increased
reaction time, and (2) the virtual reality high condition would induce increased stress compared to the other two
tasks, indicated by decreased heart rate variability and increased skin conductance response.
The virtual scene was viewed with an Oculus Rift headset and subject leg movements were tracked using a
Microsoft Kinect to show the subject’s body in virtual reality. Physiological measurements included
electrocardiography, skin conductance, and electroencephalography. During the experiment, subjects pressed a
button in response to an auditory stimulus. Analyzing reaction time from this task has been found to be an index
of cognitive load and task difficulty [2]. Mean heart rate, wrist skin conductance, and ankle jerk indicate that the
virtual reality conditions were more physically demanding compared to the real world task, while the reaction
times indicate that the virtual tasks were more cognitively demanding. Heart rate low frequency power can be
indicative of increased vagal tone [3], which may indicate that virtual reality high condition was behaviorally
different than the other two. These results indicate that the virtual reality tasks were more physically and
cognitively demanding than the real world task, while heart rate low frequency power indicates that changes in
behavior may have been elicited using immersive virtual reality. Our results suggest that even a high-fidelity
virtual reality headset can induce increases in stress and cognitive load during motor task training compared to
real world motor task training.
Table 1
Mean Heart Rate* (bpm)
Heart Rate Low Frequency Power* (%)
Wrist Skin Conductance *+ (counts/sec)
Reaction Time* (sec)
Ankle Jerk* (m/s3)
*
Significant ANOVA
Real World
93.91±2.26
54.68±2.74
0.14±0.04
1.80±0.04
16.75±0.70
Virtual Reality Low
99.31±2.63
48.72±3.36
0.24±0.05
1.97±0.06
21.62±1.12
Virtual Reality High
99.65±3.13
41.07±3.05
0.22±0.04
1.94±0.05
20.38±1.11
Pairwise Significance
RW-VR; RW-VRH
RW-VRH; VR-VRH
RW-VR
RW-VR; RW-VRH
RW-VR; RW-VRH
+
Significant ANOVA with order effects
Table 1: Physiological results are shown for the real world (RW), virtual reality low (VRL), and virtual reality
high (VRH) conditions. Low frequency power spans 0.04 to 0.15 Hz. Values are shown as mean±standard error
(n = 20). Pairwise significance is a Bonferroni post hoc test using ANOVA with repeated measures (p<0.05).
References
1. Schniepp R (2014) Quantification of gait changes in subjects with visual height intolerance when exposed to
heights. Front Hum Neurosci 8:1-8.
2. Teasdale N (1993) On the cognitive penetrability of posture control. Exp Aging Res 19(1):1-13.
3. Thayer JF (2007) The role of vagal function in the risk for cardiovascular disease and mortality. Biol
Psychol 74(2):224-242.
Acknowledgments
This work was supported by the Army Research Lab and by the NSF GRFP (Grant No. DGE 1256260).
Mediolateral postural responses to anteroposterior translations in stroke survivors
Troy J. Rand1 ([email protected]), Pierre Fayad2, Mukul Mukherjee1
1
University of Nebraska at Omaha, Omaha, NE, USA
2
University of Nebraska Medical Center, Omaha, NE, USA
Introduction: Standing postural control is a complex mechanism involving sensory input, multisensory
integration, and motor outputs all with the goal of maintaining upright posture. Being adaptable in standing
posture is important for dealing with changing environments and postural demands. A large body of literature
examins how individuals respond to continuous postural perturbations. However, the majority of this research
uses sinusoidal translations which may not mimic types of postural demands encountered in real life situations.
Furthermore, the dependent variables are usually focused on the magnitude of movement while ignoring the
temporal structure. Recent research using different temporally structured stimuli demonstrated that healthy
individuals can adapt the structure of their posture towards the structure of the support surface movements [1].
The purpose of this research is to investigate the effects of providing support surface translations with different
temporal properties on the magnitude and structure of center of pressure (COP) in a population of stroke survivors.
Results: The results from the RMS and sample entropy are provided in figure
1. The RMS values of AP sway were increased for all translation conditions.
The entropy values increased in both the AP and ML sway in the three noise
conditions but not for the sine wave condition.
Methods: Twelve chronic stroke survivors participated in this study (8M/4F; Age: 57 ± 9 years; Height: 167.5 ±
14.8 cm; Weight: 86.6 ± 27.7 kg). Participants completed three minutes of standing during five separate
conditions. These conditions were normal standing and four types of support surface translations in the
anteroposterior (AP) direction: white noise, pink noise, brown noise, and sinusoidal. The COP signal was
analyzed in both the AP and mediolateral (ML) direction. The magnitude of variability was measured using root
mean square (RMS) and the temporal structure of variability was measured using sample entropy.
Figure 1: RMS increased in the
direction of translation for all
signals. Entropy increased in both
directions only for the nonperiodic signals.
NN – No Noise, WN – White
Noise, PN – Pink Noise, BN –
Brown Noise, SW – Sine Wave. #
Discussion: The magnitude of movement responded as expected, with
increases in the RMS values in the direction of movement. Interestingly the
entropy analysis demonstrated that the COP pattern became more disordered
in both the AP and ML directions, but only in the non-periodic translations.
This could indicate that participants needed to explore the environment more
in these conditions but not in the sine wave condition. Because exploration is
beneficial in a learning paradigm it may be more beneficial to use non-periodic
sensory input in a rehabilitation setting. Further exploration needs to be done
among the noise conditions. Exploring different frequencies and/or amplitudes
of translation may result in differences emerging. Furthermore, this research
highlights the importance of exploring the structure and magnitude of
movement when analyzing postural responses to sensory input.
References
1.Rand, T., et al. Temporal Structure of Support Surface Translations Drive
the Temporal Structure of Postural Control During Standing. Ann. Biomed.
Eng., 2015.
Acknowledgments
Work supported by the Center of Biomedical Research Excellence grant
(1P20GM109090-01) from NIGMS/NIH.
Altered Rheological Properties of Passive Skeletal Muscles in Chronic Stroke
Ghulam Rasool ([email protected]), 2Allison B. Wang, 1William Z. Rymer and 2Sabrina S. M. Lee
1
Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA
2
Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago IL, USA
1
We are investigating changes in rheological properties of skeletal muscles in the hemiplegic chronic stroke
population. Our objective was to quantify viscoelastic properties of stroke-affected muscles using ultrasound
shear wave velocity as a surrogate for tissue mechanical properties. We hypothesized that as a result of the
cerebrovascular accident, in addition to well-known changes in neural and contractile properties, the local muscle
rheological properties were also changed. We also hypothesized these changes in rheological characteristics of
muscle were linked to clinically observed hypertonia, spasticity, muscle weakness, poor biomechanical output,
and impaired motor control.
We quantified rheological properties of the biceps muscle by measuring the shear wave propagation group
and phase (dispersion) velocities. The group velocity represented an average propagation speed of the shear wave
over all frequencies and quantified tissue elasticity while the phase velocity represented frequency-dependent
viscous components. Ten hemiplegic stroke survivors (5 females, 5 male) participated in the study, age 58±10
yrs., (mean±SD), Fugl Meyer range 9 to 52, and modified Ashworth Scale 0 to 3. Using SuperSonic Imaging
technology, we generated shear waves and measured their propagation in biceps muscles in a passive state [1].
We processed shear wave data to calculate group and phase velocities from both arms of stroke survivors.
Affected Biceps
*
3
Contralateral Biceps
*
*
*
*
*
2
*
*
*
1
*
Phase Velocity (m/s)
Shear wave group velocity (m/s)
4
5
4
3
2
1
0
4
Affected
Subject - 3
0
200
400
Contralateral
600
800
Subject - 10
3
2
1
0
0
Stroke participants
A
Group Velocity Data
0
200
B
400
600
Frequency (Hz)
Phase Velocity Data
800
Figure 1: (A) Group velocity
data from ten stroke survivors
is presented. We note a
significant increase in the
group velocity values on the
affected side (p<.05) except
subject 8. (B) Phase velocity
(dispersion) data for two
representative stroke
participants (3, 10) is shown.
We note that stroke-affected
muscles are relatively more
dispersive especially at higher
frequencies.
We observed significantly higher group velocity values in stroke-affected muscles (except subject 8) (Fig.
1(A)) [2]. We present phase velocity data from two representative stroke survivors in Fig. 1(B). In both cases, we
observed frequency-dependent changes in the shear wave propagation, which provided evidence of a significant
contribution from viscous components. Therefore, an analysis of muscle passive mechanical properties would be
incomplete without characterization of viscous components. We further noted that phase velocity (dispersion)
values were significantly greater in stroke-affected muscles, especially at higher frequencies, which highlighted
the importance of measuring rheological properties over elastic only [2].
We conclude that alterations in skeletal muscle post-stroke were mediated by changes in tissue rheological
parameters, i.e., in both elastic and viscous components, that may have originated from changes in the
extracellular matrix, or from connective tissue infiltration giving rise to tissue fibrosis. These alterations, in part,
played a significant role in inducing muscle weakness and caused impaired motor control after brain injury.
References
1. T. Deffieux, et al. (2009) Shear wave spectroscopy for in vivo quantification of human soft tissues viscoelasticity, IEEE TMI, 28 313-22.
2. S. SM. Lee, et al. (2015) Quantifying changes in material properties of stroke-impaired muscle. Clin
Biomech 30, 269-75.
Acknowledgments
The study was supported by the Brinson Foundation and NIH K12HD073945.
Robust Wireless Real-Time Data Transmission for Robot Control in Neurorehabilitation
Georg Rauter ([email protected]), 1,2Mathias Bannwart, 3Peter Lutz, 2Marc Bolliger and
3
Maurus Gantner
1
Sensory-Motor Systems (SMS) Lab, ETH Zurich, 8092 Zurich, Switzerland
2
SCI Center, University Hospital Balgrist, 8008 Zurich, Switzerland
3
Lutz Medical Engineering, 8455 Rüdlingen, Switzerland
4
University of Basel, Basel, Switzerland
Wireless data transmission is hardly used for direct control of robots in general. Particularly not in robots that
are applied in human-machine interaction and even less in human-machine interaction in the medical sector.
The main reasons why cable-bound data transmission remains the gold standard in human-machine interaction
and haptics are: i) high and ii) constant data rates (usually 1[kHz]) iii) without data loss are required to render
virtual environments reliably; iv) wireless transmission requires batteries and v) monitoring the charging status.
However, cable-bound data transmission has also significant disadvantages: i) long cables add extra weight to
the robotic device, ii) the dynamic behavior of the robot can be modified due to the spring-like behavior of the
cables, and iii) for large robots, even additional motors may be required for cable guidance, which iv) increases
material costs, v) space requirements and vi) energy consumption of the robotic device.
1,2,4
In our case, we are developing a large scale tendon-based robot for rehabilitation of gait disorders: the FLOAT
V2.0. The FLOAT will be applied for spinal cord-injured patients or stroke patients and is based on our
previous work [1]. Importantly, the FLOAT can provide constant body weight support during free walking,
which is known to be beneficial in neural gait rehabilitation [2]. To enable training with constant body weight
support, the end-effector force needs to be measured for force control. In the previous version of the FLOAT,
force data was transmitted by cables. Due to the advantages of wireless data transmission, particularly with
respect to the large workspace of the FLOAT (3.5x12x5m), we chose to apply wireless data transmission.
To account for possible data loss due to wireless data transmission, we have placed one radio frequency sender
in the end-effector of the FLOAT (16h operational) and two receivers in opposite corners of the room. Both
receivers are connected to the same EtherCAT network that records data at 1[kHz]. In case one receiver cannot
properly receive data, the other receiver will kick in. In this way, data loss can be reduced to an acceptable and
stable minimum, which could be shown in a ten minutes experiment for a lying and a moved sender (Figure 1).
Figure 1 A,B: Loss of
wireless transmitted real-time
data in consecutive time
frames. Data loss varies based
on the data recorded from
receiver 1, receiver 2, or the
combined signals of both
receivers. Data was recorded
in real-time at a data rate of
1[kHz]. Figure 1A shows consecutive data loss when the sender is lying (max consecutive data loss
receiver1=10, receiver2=24, combined receivers=2). Figure 1B shows data loss when the sender is moved (max
consecutive data loss receiver1=77, receiver2=70, combined receivers=7).
References
1. Vallery H., et al. (2013) Multidirectional transparent support for overground gait training. ICORR pp 1-7.
2. Dominici N., et al. (2012) Versatile robotic interface to evaluate, enable and train locomotion and balance
after neuromotor disorders. Nature Medicine 18:1142–1147
Acknowledgments
This work was supported by the Swiss CTI Project ”17567.1 PFLS-LS”.
A
B
Control of redundant musculoskeletal systems using muscle synergies
Reza Sharif Razavian ([email protected]) and John McPhee
Systems Design Engineering, University of Waterloo, Canada
Humans can perform a task in multiple ways (kinematic redundancy), and for each motion, there are an infinite
number of solutions for muscle activations (dynamic redundancy). Muscle synergy theory has been proposed to
address the challenge of dynamic redundancy [1]; however, its usefulness in motion control and its relation with
kinematic redundancy has not been thoroughly investigated. We have proposed a comprehensive synergy-based
framework for the control of musculoskeletal systems that simultaneously handles the kinematic and dynamic
redundancies. It allows for fast calculation of muscle activations to perform a task, without the need to solve an
optimization problem.
The proposed framework is shown in Fig 1(A). In this hierarchical structure, the feedback control occurs in the
task space. This high-level controller specifies the corrective signal (the needed accelerations in the task space,
aref), using robust, optimal or even error-driven (e.g. PID) control logics. In the low-level controller, the
acceleration signal is translated into muscle activations using muscle synergies. This framework is based on the
fact that each synergy has a known effect in the task space, and we assume that the nervous system knows the
acceleration vector that each synergy produces. The set of all the synergy-produced acceleration vectors form a
basis set for the task space. An arbitrary task space acceleration can be decomposed onto this basis set with little
computational effort. We can then combine the synergies with the calculated coefficients to find the muscle
activations that produce the desired task space acceleration.
In the proposed framework, the control of task-related degrees of freedom is separated from the redundant ones
(defined by Uncontrolled Manifold). We have considered two types of synergies: the ones that only produce task
space accelerations, and the ones that only affect the motion in the redundant space. Therefore, the high-level
controller may only deal with the task space variables using the task-related synergies, and neglect the control of
the redundant variables. One important assumption in this framework is the dependence of the synergies on the
desired task space. For example, the synergies for a 3-dimensional reaching task would be different from a 1dimensional elbow flexion, because of the difference in the task space. We hypothesize that for efficient control
of motion, the nervous system knows multiple sets of synergies to use in different tasks.
In Fig 1(B) we have provided simulation results for motion control of a musculoskeletal arm, when the hand
moves 20 cm up from a resting position. The model has three degrees of freedom in the task space (3D position
of hand), and one extra degree of freedom (elbow motion) which is left uncontrolled. The synergy-based
framework produces near-optimal results, with 90-95% reduction in computation time.
(A)
(B)
Fig 1. (A) The synergy-based framework for motion control. (B) The simulation results. The hand starts to move upward at t=0.5 s.
References
[1] Bizzi, E., Cheung, V. C. K., D’Avella, A., Saltiel, P., & Tresch, M. C. (2008). Combining modules for
movement. Brain Research Reviews, 57(1), 125–33.
1
Subject-specific Surrogate Models of Task-level Human Movement
Jeffrey A. Reinbolt ([email protected]), 1 Nicolas Vivaldi, and 2 Misagh B. Mansouri
1
University of Tennessee, Knoxville, TN, USA
2
How appropriate neural control inputs are selected to achieve a biomechanical movement task output is an open
question. Experiments have exposed many aspects of human movement; however, simulations can complement
experiments to help uncover task-level principles of coordinated and uncoordinated movements for predictions
of functional outcomes. Predictions of subject-specific movements require full-body musculoskeletal modeling,
accurate dynamic simulation, and robust control systems design. It is well known that human movement
involves closed-loop control; therefore, a closed-loop blend of biomechanics and robotics approaches offers
great potential for accelerating the study of human movement control and subject-specific outcome prediction.
For this blended closed-loop coordination, we have been combining biomechanical motions with surrogate
ൌ ܾ଴ ൅ σଶ௜ୀଵ ܾ௜ ࢞௜ ൅
response surfaces [1], developed using a second-order polynomial model (࢟ୢୣୱ୧୰ୣୢ
୲ୟୱ୩
ଶ
ܾଵଶ ࢞ଵ ࢞ଶ ൅ σ௜ୀଵ
ܾ௜௜ ࢞ଶ௜ ), of task-level neural control (Fig. 1a, 1b). Separate surfaces (Fig. 1b, 1c) are created for
desired subtasks (e.g., swing foot position) as a function of a primary task (e.g., center of mass, CoM, balance).
Each response surface finds a set of polynomial coefficients to best fit the subject’s data. Desired tasks are
computed from response surfaces as surrogate models for subject-specific motion coordination.
We created subject-specific surrogate models using three-dimensional (3D) motion capture data of balance
recovery in unimpaired adults (female 25 yrs | 68.0 kg; male 25 yrs | 84.5 kg) and a range of walking speeds in
unimpaired children (6 female | 2 male | 12.9±3.3 yrs | 51.8±19.2 kg) [2]. This data defined the high-level
control relationships between tasks, where the response of a variable of interest (y) is influenced by a set of
predictors (xi). We related the 3D swing foot (Fig. 1c, v2) and torso (v3 not shown) positions to the CoM position
in the transverse plane over the base of support (Fig. 1c, v1).
Surrogate models using quadratic surfaces accurately predict the responses for a range of adult and child
movement data. These response surfaces advance our current understanding of the biomechanics and neural
control of movement by establishing prioritized tasks for different motions and defining surrogates for these
tasks; moreover, they may allow synthesis of a range of
specific-subject motions without the need for
additional prospective motion capture data
(e.g., prediction of post-treatment
outcome from pre-treatment motion).
References
1. Box G E P (1951) On the
experimental attainment of
optimum conditions. J R Stat
Soc B 13:1–45.
2. Liu M Q (2008) Muscle
contributions to support and
progression over a range of
walking speeds. J Biomech 41:
3243-3252.
Acknowledgments
Supported by NSF #1253317.
Figure 1: Subject-specific surrogate models created from experimental
motion capture data and representing desired task-level coordination.
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ULJKW)/%5%%/)5YHUVXVEDFNULJKWEDFNOHIW%5%/DQG&%5%/YHUVXV)/%5)RUHDFKJDLWVSDFHSURMHFWLRQVKRZQORFDWLRQVRIGLIIHUHQWJDLWVDUH
ZLWKLQSODQHH[FHSWIRUZDONDQGWURW7KHRIIVHWVIRUHDFKDUHDVIROORZV$WURW ߨ ٔZDON ͵ߨൗʹ ٔ%WURW ߨ ٔZDON ߨൗʹ ٔ&WURW ߨ ٖZDON ߨ ٖZKHUHٔDQGٖUHSUHVHQWLQWRDQGRXWRIWKHSDJHUHVSHFWLYHO\'DVKHGOLQHVLQDOOILJXUHVUHSUHVHQWWKHILUVWSULQFLSDOFRPSRQHQWRIDOOGDWDLQ'VSDFH
SURMHFWHGRQWRD'SODQH,QDOOILJXUHVGRWFRORULVUHSUHVHQWDWLYHRILQGLYLGXDODQLPDOVQ DQGHDFKGRWUHSUHVHQWVRQHVWULGHSRVWSHUWXUEDWLRQN Long-term training modifies the modular structure and organization of walking balance control
1
Andrew Sawers ([email protected]), 2Jessica L. Allen, and 2Lena H. Ting
1
University of Illinois at Chicago, Chicago, IL, USA
2
Emory University, Atlanta, GA, USA
How long-term training affects the neural control of motor behaviors is not well understood, but may reveal
previously unknown mechanisms of motor coordination and learning that could guide future rehabilitation
efforts. Therefore, our goal was to determine how the structure and organization of muscle coordination patterns
for walking and balance are affected by long-term training. We hypothesized that long-term training leading to
skilled motor performance increases the recruitment of common muscle patterns across different motor
behaviors. In lieu of searching for behavior-specific or optimal muscle patterns, generalizing the same muscle
patterns across behaviors may enable rapid, reliable, and efficient identification of motor solutions.
To test this hypothesis we recruited 13 professional ballet dancers (experts) and 10 untrained novices. We used
muscle synergy analysis to quantify and compare the structure and organization of their muscle coordination
patterns during overground walking and a challenging beam-walking task designed to assess walking balance
proficiency by evoking balance failures [1].
Consistent with our expectation that experts would have better walking balance, experts walked farther than
novices on the narrow beam (experts: 0.91 ± 0.06; novices: 0.71 ± 0.09; P = 0.03). During beam walking
experts recruited more muscle synergies than novices (experts: 6.69 ± 0.60; novices: 5.60 ± 1.15; P = 0.009),
suggesting a larger motor repertoire. In contrast, the number of muscle synergies recruited during overground
walking did not differ between groups (experts: 7.00 ± 0.82; novices: 6.30 ± 1.16; P = 0.05), but their
composition did, suggesting that extended practice on one behavior (ballet) can alter the control of another
(walking). Muscle synergies in experts had less muscle co-activity and were more consistent than in novices
during beam and overground walking, reflecting greater efficiency in muscle output during trained and
untrained activities. Moreover, the pool of muscle synergies shared between beam and overground walking was
larger in experts than novices (experts: 82 ± 18%; novices: 54 ± 22%; P = 0.02), suggesting greater versatility
of muscle synergy function across behaviors. These differences in motor output between experts and novices
could not be explained by differences in kinematics. Thus, they likely reflect differences in the neural control of
movement following years of training rather than biomechanical constraints imposed by beam walking or
musculoskeletal structure and function.
The recruitment of common muscle synergies between beam and overground walking by experts suggests that
to learn challenging new behaviors we may take advantage of existing muscle synergies used for related
behaviors and sculpt them to meet the demands of a new behavior rather than create de novo behavior specific
muscle synergies. This is consistent with early stages of skill learning in animals that involve reconfiguring
existing motor patterns [2]. Successful rehabilitation may require therapies that train patients to recruit common
muscle synergies across motor behaviors rather than behavior specific motor solutions.
References
1. Sawers A and Ting LH (2015) Beam walking can detect differences in walking balance proficiency across a
range of sensorimotor abilities. Gait and Posture 160:55–69.
2. Kargo WJ and Nitz DA (2003) Early skill learning is expressed through selection and tuning of cortically
represented muscle synergies. J. Neurosci 23:11255–69.
Acknowledgments
Supported by National Science Foundation (Emerging Frontiers in Research and Innovation) Grant 1137229,
and National Institutes of Health Grants HD-46922, T32 NS-007480-14, and F32-NS087775.
Independent Component Analysis of EEG Can Detect Neural Correlates of Stress
1
Bryan R. Schlink ([email protected]), 1Steven M. Peterson and 1Daniel P. Ferris
1
Traditional measures of acute stress have several shortcomings, including poor temporal resolution, invasiveness,
and the potential to cause iatrogenic stress based on the measurement technique [1]. Electroencephalography
(EEG) has good temporal resolution and is minimally invasive, potentially providing a means to monitor acute
stress. A few studies have investigated EEG electrode data for indications of stress, but an alternative is to
combine independent component analysis (ICA) with brain source localization with an inverse head model. The
purpose of this study was to determine whether EEG with ICA could be used to monitor acute stress responses in
a real-world motor task. We examined healthy young subjects conducting a shooting task with an airsoft rifle,
under conditions of Nonstress (normal shooting) and Stress (shooting while being shot at). We used traditional
physiological measures of acute stress (salivary cortisol, electrodermal activity, and heart rate) as a means to
assess overall stress level.
11 healthy male volunteers between the ages of 19-30
years participated in the study. Subjects performed a
shooting task with an airsoft rifle in our laboratory. The
experiment consisted of two conditions (Nonstress and
Stress) that were repeated twice. In each condition, we
instructed subjects to aim the rifle at a target and fire one
shot, repeating the process until they had fired 50 shots.
However, in the Stress condition, an experimenter used
a different airsoft rifle to fire shots in the subject’s
direction. We recorded electrodermal activity, salivary
cortisol, heart rate, and EEG data during the experiment.
We found that subjects had higher skin conductance
responses (SCRs) (p<0.02) and higher salivary cortisol
levels (p<0.04) during the Stress condition compared to
the Nonstress condition. These values suggest that the
Stress condition induced acute stress in the subjects.
EEG data analysis showed five independent component
clusters with significant shifts in spectral power (Fig. 1).
Specifically, large changes in spectral power could be
seen in the alpha band for the somatosensory association
complex 1-2 seconds after the trigger pull. Additionally,
changes in the pre-motor and supplementary motor
cortex were observed in the high gamma range
immediately after the trigger pull. Overall, the results
from this experiment suggest that ICA of EEG could be
used in real world situations to quantify acute stress.
Future studies could investigate differences between
Figure 1. Spectral power plots for the Stress
naïve and experienced subjects, as well as the effects that
condition (left), Nonstress condition (middle), and
training has on an individual’s level of stress.
the difference of the two (right) for five independent
component clusters (rows). The vertical black line
indicates the time the subject pulled the trigger.
References
1. Hellhammer DH, Wust S, Kudielka BM (2009). Salivary cortisol as a biomarker in stress research.
Psychoneuroendocrinology 34(2): 163-171.
Static Optimization vs. Computed Muscle Control Characterizations of Neuromuscular Control:
Clinically Meaningful Differences?
Sarah A. Schloemer ([email protected]), Elena J. Caruthers, Rachel K. Baker, Nicholas C. Pelz,
Ajit MW Chaudhari, and Robert A. Siston
The Ohio State University, Columbus, OH, USA
OpenSim [1] is commonly used to extend beyond traditional inverse dynamics calculations to analyze
movement at the neuromuscular level. Two optimization tools, Static Optimization (SO) [2] and Computed
Muscle Control (CMC) [3], are often used to estimate muscle activations and forces. However, it is unknown to
what degree muscle activation and force estimates are affected by optimization tool and model choice. The
purpose of this study was to determine how SO and CMC affect muscle activation and force estimates during
gait in two models: Gait2392 (distributed with OpenSim) and Full-body OpenSim Model (Hamner) [4].
In OpenSim 3.1, six healthy young adults from a previous study [5]
were scaled in each model. Then, a gait trial was processed through
Inverse Kinematics, Residual Reduction Algorithm, SO, and CMC to
reproduce gait kinematics and estimate muscle forces and activations.
Peak muscle activations and forces were calculated for each SO and
CMC trial. Co-contraction indices (CCIs) were calculated [6] for the
lateral (VLLH) and medial (VMMH) vasti and hamstrings, and lateral
(VLLG) and medial (VMMG) vasti and gastrocnemius (gastroc).
Repeated measures two-way ANOVAs (α<0.05) and Tukey post-hoc
tests assessed for differences in peak activations, forces, and CCIs
between models and optimization tools.
CMC peak forces (Fig. 1A) and activations (Fig. 1B) tended to be
higher than those of SO both within and between models. In contrast,
Hamner’s CMC gastroc force was less than all other conditions by
704-911 N. CMC also estimated greater muscle co-contractions, with
up to 10 times greater CCI in Gait2392 (Fig. 1C). Since greater CCIs
are often associated with pathologies, such as knee osteoarthritis [6],
differences in muscle forces and activation strategies from simulations
using SO or CMC may impact clinical interpretation of simulations.
Thus, this study emphasizes the need for a more subject-specific
optimization method for characterizing neuromuscular function in a
given population.
References
1.Delp SL (2007) OpenSim: open-source software to create and
analyze dynamic simulations of movement. IEEE Trans Biomed Eng. 54:1940-50.
2.Anderson FC and MG Pandy (2001) Static and dynamic optimization solutions for gait are practically
equivalent. J Biomech. 34:153-61.
3.Thelen DG (2003) Generating dynamic simulations of movement using computed muscle control. J Biomech.
36:321-8.
4.Hamner SR (2010) Muscle contributions to propulsion and support during running. J Biomech. 43:2709-16.
5.Thompson JA (2013) Gluteus maximus and soleus compensate for simulated quadriceps atrophy and
activation failure during walking. J Biomech. 46:2165-72.
6.Lewek MD (2004) Control of frontal plane knee laxity during gait in patients with medial compartment knee
osteoarthritis. Osteoarthritis Cartilage. 12:745-51.
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The metabolic cost of changing walking speeds is significant and implies lower optimal speeds for shorter
distances
1 Nidhi Seethapathi ([email protected]) and 2 Manoj Srinivasan
1,2 The Ohio State University, Columbus, OH, USA
Normal human walking involves starting, stopping, and changing speeds. Although much is known about
constant-speed walking, the cost of changing speeds has not been measured without non-inertial treadmill speed
changes. Here, we measure the metabolic energy cost of walking when changing speeds on a constant-speed
treadmill. Most daily walking appears to happen in short bouts, starting and ending at rest. Based on the
metabolic cost of changing speeds, we predict lower walking speeds for short walking bouts.
Experimental and computational methods: Subjects (N=16) walked with oscillating speeds on a constantspeed treadmill while metabolic energy and hip motion measurements were made. They alternately walked
faster and slower than the belt (see figure 1). Oscillating-speed trials were at one or both constant treadmill
speeds 1.12 m/s and 1.56 m/s. Other subjects (N=10) were asked to walk over short distances (2 to 14 m) at a
comfortable speed, starting and ending at rest; the average speed taken to cover the distance was found. The
metabolic rate increase was compared with two models (inverted pendulum and simple KE fluctuations)
Results: Metabolic rate of oscillating-speed walking was significantly higher than constant-speed walking (6 to
20% cost increase for 0.13 to 0.27 m/s speed fluctuations). The measured kinetic energy fluctuations of the hip
were correlated with but, over-predicted the increase in metabolic cost for changing speeds. Optimizing the total
metabolic cost for walking a given distance predicts a lower optimal speed for shorted distances. In overground
walking experiments, we found lower walking speeds for shorter distances, as predicted. Analyzing published
daily walking data, we estimate the cost of changing speeds is 4-8% of daily walking energy budget.
L
Treadmill belt
at constant
speed
Treadmill
rear
Bungee cord prescribes
maximum walk excursion
Treadmill
front
Figure 1: Subjects walking with changing speeds,
moving between two positions in the lab frame
Acknowledgments. Supported by NSF 125482.
Figure 2: The preferred walking speed over short
distances is low, as predicted.
1
Leg impulse control in human running
Nidhi Seethapathi and 1Manoj Srinivasan ([email protected])
Constant-speed human running is not exactly periodic. For instance, the body states of the person at mid-flight
fluctuate about a mean value. Despite these noise-like deviations, people are able to run without falling down.
Here, we examine how these natural state fluctuations are controlled using ground reaction forces. As in [1,2],
we use natural step-to-step variability to infer such control. In contrast to [1], which attempted to explain
running stability with variants of a spring-mass model, we directly focus on ground reaction forces modulations.
Methods. Subjects (N = 8, 3 female, 5 male) ran on a treadmill at 2.5 m/s while motion and ground reaction
forces (force treadmill) were collected for a few minutes. For this data, we computed the impulse (time-integral)
of the ground reaction forces for each step and each left-right stride. Using linear least squares methods similar
to [1,2], we obtained a linear model between the deviations in the average hip states at each mid-flight phase
(input) and the corresponding deviations in the ground reaction impulses for the immediately following step or
the immediately following stride (output). Mid-flight event is defined as the instance when the vertical position
of the hip is locally maximum. For each subject there are around 800 mid-flight events. See Fig 1 for coordinate
notation.
Results: The linear models averaged over all the subjects suggest the following control strategies for leg
impulse. We find that 85% of the deviation in the sideways speed at mid-flight is nullified by a corrective
sideways impulse in the following foot-strike, and is completely nullified over a full stride. Similarly, 60% of
the deviation in the fore-aft speed is nullified by a corrective fore-aft impulse in the following foot-strike and is
completely nullified over a stride. Further, we find that, when a change in the fore-aft speed is achieved, it is
done by changing the negative part of the fore-aft ground reaction force by a larger amount than the change in
the positive part (see figure 1). Further, inferring a linear model from both mid-flight position and velocity to
the next step’s impulse suggests that the velocity deviations are more important determinants of the impulse
than positions. Current work involves inferring a more detailed controller for human running from data.
Figure 1: A linear map from flight state to stance forces is inferred. If the runner is going too fast or too slow at
mid-flight, this deviation is corrected by fore-aft leg impulse modulation, changing negative impulse more than
positive impulse.
References
1. Maus, HM, et al. Constructing predictive models of human running. J. Roy. Soc. Interface (2015).
2. Wang, Y and Srinivasan, M. Stepping in the direction of the fall: the next foot placement can be predicted
from current upper body state in steady-state walking, Biology Letters, (2014).
Acknowledgments. Supported in part by NSF 1254842 and Schlumberger Faculty for Future fellowship.
Interaction of Muscle Coordination and Internal Knee Mechanics during Movement
1
Colin R Smith ([email protected]) and 2Darryl G Thelen
1
Neuromuscular coordination and internal knee tissue mechanics are inherently coupled. This coupling is
especially apparent in the pathologic knee, where ligament and cartilage loads are highly dependent on muscle
loading, and neuromuscular coordination is often altered to accommodate pathologic joint behavior. A better
fundamental understanding of this coupling during locomotion could improve both orthopedic and neuromuscular
retraining treatments for pathologies such as anterior cruciate ligament injury and osteoarthritis. The complex
coupling of muscle and soft tissue loading about the knee creates potential for computational modeling to provide
valuable insight. Musculoskeletal models have been created to study neuromuscular coordination in movement,
but often use a highly simplified representation of the knee joint. At the other extreme, complex knee models
predict the interaction of muscle, ligament and cartilage tissue loads without considering neuromuscular
coordination. We have developed a novel multibody knee model and probabilistic simulation framework to study
the interaction of neuromuscular coordination and internal knee mechanics during movement.
We constructed a three body knee model that included 6 degree of freedom tibiofemoral and patellofemoral joints.
Cartilage surfaces and ligament attachments were segmented from MR images of a healthy adult female. Fourteen
ligaments were represented by bundles of nonlinear elastic springs. Cartilage contact pressures were calculated
using an elastic foundation model. The knee model was integrated into a generic musculoskeletal model and
validated by comparing simulated knee kinematics with in vivo kinematics measured by dynamic MRI [1].
Our simulation routine predicts internal knee tissue loads from kinetic and kinematic measurements of gait. At
each time step, an optimization routine termed COMAK (concurrent optimization of muscle activations and
kinematics) calculates the muscle forces, patellofemoral kinematics and secondary tibiofemoral kinematics that
minimize a weighted sum of squared muscle activations while satisfying overall dynamic constraints. The
constraints require that the muscle forces and internal knee loads (contact pressures, ligament forces) generate the
measured hip, knee (flexion) and ankle accelerations [2].
To investigate the influence of neuromuscular coordination on knee behavior, we performed a parametric Monte
Carlo analysis of 2000 simulations that randomly varied isometric muscle strengths by up to ±60% of the values
in the nominal model. By varying the muscle strengths, the COMAK algorithm produces variable neuromuscular
coordination strategies. Our simulations show that predicted muscle forces exhibit greater variability than net
cartilage tissue loads. We are now investigating alternative optimization cost functions that induce co-contraction
to better understand how joint stiffening can alter the magnitudes and locations of cartilage contact stresses.
Figure 1:
a) Multibody knee model.
b) Tibial cartilage contact pressure at
peak gastrocnemius loading.
c) Mean and 95% CI of muscle and
knee joint loading during the stance
phase of gait.
References
1. Lenhart RL (2015) Prediction and Validation of Load-Dependent. Ann Biomed Eng 43(11):2675–85.
2. Smith CR (2016) Influence of Ligament Properties on Tibiofemoral Mechanics. J Knee Surg 29(2):99–106.
Acknowledgments NIH EB015410 and HD084213
Connectivity Fluctuations During Viewed and Performed Rhythmic Movements of the Arms and Legs
1,2
Kristine L. Snyder, 1,3Julia E. Kline, 1.4Helen J. Huang, 1Daniel P. Ferris
1
2
University of Minnesota Duluth, Duluth, MN, USA
3
FitBit, San Francisco, CA, USA
4
University of Central Florida, Orlando, FL, USA
There has been a recent focus on research utilizing mobile brain imaging techniques, particularly during
walking [1]. Many of these studies have the ultimate goal of reading neural signals corresponding to the
intention to move. However, it is difficult to separate intention from performance, and most studies on intention
have focused on arm movement, not leg or full body movement. To determine how different areas of the brain
interact to move the arms and legs, we analyzed brain data while subjects both watched videos of and
performed rhythmic arm and leg movements.
We collected data from 10 subjects. Each subject performed a series of recumbent stepping trials in random
order. These trials included both performing and watching videos of rhythmic movements with just the arms,
just the legs, and the arms and legs together. We then performed independent component analysis and dipole
fitting to determine which areas of the brain were active during these real and viewed movements. We
calculated connectivity using the directed transfer function, and performed a fast Fourier transform on these
data to identify the frequencies at which connectivity fluctuated.
We found connectivity fluctuations between specific brain areas that oscillated at a frequency that corresponded
to the recumbent stepping (or reaching) frequency (Figure 1). These patterns occurred whether subjects
performed or viewed these movements and whether they used arms, legs, or arms and legs. This network
appeared to be driven by the right premotor and the supplementary motor cortices.
Figure 1: A Fourier analysis of connectivity between different brain
regions during while subjects watched a movie of themselves
performing this task revealed connectivity fluctuations (blue line)
occurring at the same frequency as the viewed movements (dotted black
lines).
These results indicate that there may be an underlying network of brain areas during both viewed and performed
rhythmic human movements. Future work could examine whether these same patterns occur in walking.
References
1. Gramann, Klaus, et al. (2011) "Cognition in action: imaging brain/body dynamics in mobile humans." Rev
Neurosci 22(6): 593-608.
Acknowledgments
Supported in part by Army Research Laboratory (ARL; W911NF-10-2-0022) and the National Institutes of
Health (NIH; R01 NS-073649). We thank Taylor Southworth and Bryan Schlink for assisting with data
collection.
Two observations that suggest that metabolic cost is not a key determinant of gait parameters
Knoek van Soest ([email protected]), Ramon Aartsen, Axel Koopman, Dinant Kistemaker, Maarten Bobbert
Department of Human Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands
Humans are commonly suggested to select gait parameters (e.g. walking speed S, step frequency F, slope during
uphill walking) such that metabolic cost (of transport (MCOT) during horizontal walking; per vertical meter
(MCVM) during uphill walking) is minimized; for level walking, it has even been suggested that selection of
gait parameters is based on real-time optimization of MCOT [1]. In this study, we describe two experimental
observations relevant to these two suggestions. Our observations concern kinematics and oxygen consumption
during level and uphill walking on a large treadmill that is controlled in real time.
Observation 1 concerns the selection of S and F during level walking; the experiment was inspired by [2]. In a
setup in which treadmill speed automatically adapted to walking speed, we first carefully determined, for each
participant, the SF-combination during preferred walking (PW), and the preferred S at 4 metronome-imposed
F's that were substantially different from PW. Next, we smartly designed a piecewise-linear SF-relation
("constraint function C") that intersected the preferred SF-relation in three points, the middle one of which
corresponded to PW. When C was active, the participant was allowed to freely select S, while the metronome
frequency was controlled based on the current value of S, such that constraint function C was satisfied. In this
mode, steady state walking was only possible at SF-combinations that satisfied C; however, the participant
could temporarily diverge from C. In the key trial, treadmill speed was initially set to the preferred S, and the
participant was asked to walk for about one minute. Then, C was activated and walking continued until both S
and F were constant for three minutes. Oxygen consumption was measured in the two minutes that followed.
Surprisingly, none of the participants continued to walk at their preferred SF-combination; the steady-state SFcombination to which a participant converged was always close to an intersection between C and (our rough
estimate of) the preferred SF-relation. MCOT at this steady-state SF-combination was 13% higher than MCOT
at PW at which the trial started (no baseline correction). Were participants at all able to exhibit PW when C was
active? We checked this in an additional trial with C active, in which we instructed participants to walk at their
preferred SF-combination, while providing, for reference, visual feedback about S. Participants had no problem
to walk at their preferred SF-combination under these conditions, and MCOT was not different from that during
normal PW. Observation 1 is hard to reconcile with the hypothesis of real-time minimization of MCOT; it lends
support to the hypothesis that an internally represented preferred SF-relation plays an important role in the
choice of S and F during level walking.
Observation 2 concerns the metabolic cost per vertical meter (MCVM) as a function of uphill walking slope, at
freely chosen S and F. MCVM was minimal when the treadmill slope was set at its maximum of 10 degrees,
which is line with [3]. When participants were instructed to cover 100 vertical meters at a self-selected slope,
none of the participants converged to the 10 degree slope at which MCVM was minimal; participants selected a
slope of 6 degrees (SD 1 degree), resulting in an MVCM that exceeded MCVM@10deg by 30%. Observation 2
suggests that MCVM is not a key concern in slope selection, which is all the more surprising if one realizes that
the energy expenditure in uphill walking is substantially higher than in level walking.
Our observations suggest that metabolic cost is not a key determinant of gait parameters. It remains to be
investigated if both observations can be explained on the basis of a single criterion, such as maximization of
endurance.
References
Selinger JC, O’Connor SM, Wong JD and Donelan JM (2015) Current Biol. 25, 2452–2456
Snaterse M, Ton R, Kuo AD and Donelan JM (2011) J. Appl. Physiol. 110, 1682-1690.
Minetti AE, Moia C, Roi GS, Susta D and Ferretti G (2002) J. Appl. Physiol. 93, 1039-1046, 2002
1.
2.
3.
How far are we from genetic neuromechanics? Tantalizing prospects and hard challenges using new
molecular tools in movement science
1
Andrew J. Spence , Simon Wilshin2, Ornella Capellari2, Kim Wells2, Ben Robertson1, Annie VahedipourTabrizi1, Dominic Wells2
1
Temple University, Philadelphia, PA
2
Royal Veterinary College, London, UK
Introduction: Movement science increasingly stresses the integration of studies of intact, freely behaving
animals. But our ability to manipulate the nervous system in intact animals is limited. An ideal manipulation
would be specific, repeatable, reversible, and for many locomotor questions, fast. Some new molecular genetic
tools, such as optogenetics [1], have the potential to satisfy these requirements, and thus to make difficult, longstanding questions in movement science more tractable. For example: How is information from different classes
of sensory input integrated and utilized during legged locomotion? Is the Henneman size principle [2] an
adaptive phenomenon from the perspective of muscle physiology? How is gait regulated to maintain stability in
the face of perturbation [3]? Recent work will be presented that is targeted at the first and third questions above,
that either currently utilizes optogenetic tools or is in the process of developing those tools.
Methods: Selective muscle activation to perturb running mouse gait: Gait control is highly dependent on speed
and locomotor phase, making perturbation of gait in moving animals difficult. Using muscle activation to
perturb gait can overcome this, but electrical stimulation of nerve elicits spurious sensory feedback. Using
optogenetics in transgenic mice, we selectively stimulated motor nerves (Fig. 1) to understand how the intact
animal recovers from perturbations with more natural peripheral sensory feedback intact.
Selective silencing of muscle spindles: The genetic targeting capabilities of neurogenetic tools make it feasible
to selectively manipulate distinct sensory afferents, such as muscle spindles or Golgi tendon organs. Here we
describe work to create the genetic constructs and delivery methods required for selective modulation of muscle
spindle afferents in rodents.
Results/Discussion: Using a transgenic mouse line and a custom miniature optical nerve cuff we perturbed
running mice using selective optogenetic stimulation of motoneurons in the sciatic nerve. Several challenges
were met, that reveal the state of the art and limitations. The rapid attenuation of visible light in peripheral nerve
(~150 µm length constant) lead to large inter-subject variability in muscle activation, due to varied proximity of
motor neurons to the nerve perimeter. Second, the transgenic mice had to be back-crossed to produce a healthy,
yet still ontogenetically susceptible, strain. These findings and our progress to date on tools for proprioceptive
manipulation highlight the need for 1) more underlying genetic knowledge and associated tools, 2) longer
wavelength or alternate methods of light delivery/actuator triggering, and 3) further characterization and
mechanistic understanding of current genetic tools, including validation with conventional approaches.
References: [1] Llewellyn, M.E. et al., (2010) Nat. Meth. 16(10). [2] Henneman, E. et al., (1965) J. Neurophys
28(3). [3] Wilshin. S. et. al., (2012) Int. Comp. Biol., 52.
Figure 1: Neurogenetic tools are making possible more selective manipulation of sensorimotor pathways (A) in freely behaving animals
(C). Transgenic ChAT::ChR2 mice express the optical activator Channelrhodopsin in motor neurons, but not sensory afferents (B; green
axons are GFP positive due to transgenic construct). An implanted optical cuff on the sciatic nerve can selectively activate motor neurons
in the intact, running animal, causing a perturbation (C). Changes in stance duration of the perturbed limb with optical stimulation (xaxis: pre-, stimulation, and post-stimulation strides; y-axis: stance duration in frames. 280 Hz video).
Why walk, trot, and gallop: energy optimality in a simple quadruped model
1
Manoj Srinivasan ([email protected])
1
Most horses generally walk at slow speeds, trot at medium speeds, and gallop at high speeds. Hoyt and Taylor
[1] showed that in horses, among the three gaits, walking has the least metabolic rate at low speeds, trotting at
medium speeds, and galloping at high speeds. Here, we use large-scale numerical optimization to demonstrate
that walking is energy optimal at low speeds, trotting at intermediate speeds, and galloping at high speeds for
simple mathematical models of a quadruped.
Models and methods. We used three quadruped models. First, we considered perhaps the simplest quadruped
model, restricted to the sagittal plane, with the upper body consisting of a single extended rigid body and four
ideal legs that can change length and apply forces on the upper body (see Fig 1). Next, we considered a more
realistic quadruped model, with a neck attached to the body in a compliant manner. Finally, we considered a 3D
analog of the first model: a single 3D rigid body torso with 4 legs. The metabolic cost model was a the sum of a
stance cost and a leg swing cost; the stance cost was the sum of terms for positive work, negative work, and leg
force; the leg swing cost was modeled as proportional to the work required to move the leg through the stride
length. For each model, we used numerical optimization to obtain energy optimal body motions and leg force
profiles for a given footfall sequence and optimized the footfall sequence from among a large set of footfall
sequence patterns (but not all such patterns). For results below, we used body parameters similar to a horse.
Results. All three models had the property that walking is energy optimal at low speeds, trotting at intermediate
speeds, and galloping at high speeds (Fig 1). While a ‘pacing’ gait is indistinguishable from a trot for the planar
model, the trot had lower cost for 3D model. Similarly, other uncommon quadruped gaits such tölt were suboptimal. The cost difference between a gallop and a trot was less than 5% in the range of speeds at which they
were optimal. We will show how the optimal gaits change as we change some body-neck-leg parameters.
Current work involves generalizing these methods to other multipedal animals (cockroaches and other insects),
and including other goal criteria than just energy minimization, e.g., improving stability.
Figure 1: A simple planar quadruped
model with a rigid upper body and four
ideal legs. Metabolic rate for the three
gaits for this simplest model: walking
(blue), trotting (red), and galloping
(black), and the regimes in which they
were optimal.
References
1. Hoyt, DF and Taylor, CR. Gait and the energetics of locomotion in horses. Nature (1981).
2. Srinivasan, M and Ruina, A. Computer optimization of a minimal biped model discovers walking and
running, Nature, (2006).
Acknowledgments. Supported in part by NSF 1254842.
Dynamic Stability to Cope with Perturbations in the Control of Complex Objects
1
Dagmar Sternad ([email protected]), 2Albert Mukovskyi, 3Julia Ebert, 2Tjeerd Dijkstra
1
Northeastern University, Boston, USA, 2University of Tübingen, Germany, 3Imperial College, London, UK
From swinging a hammer to drinking a cup of coffee, interaction with objects–-tool use–-is a skill that has provided
humans with an evolutionary advantage. When guiding a cup of coffee to one’s mouth, the actor not only exerts
forces on the cup and indirectly onto the coffee inside, but the sloshing coffee also exert forces on your hand. It
requires precise control to preempt and compensate those complex interaction forces to avoid spilling. To date,
motor neuroscience has primarily focused on simple movements like reaching to a target, or grasping static or
transporting rigid objects. However, findings from simple actions are difficult to extrapolate to tasks with complex
dynamics.
For such complex nonlinear interactions the slow neural transmission and neuromotor noise make error correction
insufficient; prediction based on internal models of complex dynamic objects seems implausible. Previous work on
continuous interactions showed that humans increased predictability of object dynamics to facilitate control. This
study examined single discrete movements and hypothesized that actors make the interaction dynamically stable to
preempt and compensate for perturbations. To evaluate stability of the trajectories, contraction analysis was applied,
as traditional Lyapunov analysis is confined to stable attractors. We expected that with practice subjects increased
the stability, or contraction of cup and ball dynamics, specifically in the presence of external perturbations.
Using a virtual set-up, we implemented a simple 2D model for the task of carrying a cup of coffee: using the
cart-and-pendulum system, the pendulum bob represented the liquid moving inside a cup defined by the bob’s
semicircular path (Fig.1A,B). Participants moved a robotic manipulandum to control the virtual cup with the
ball “rolling” inside (Fig.1C); the goal was to move the cup to a target as fast as possible without letting the ball
escape. A small perturbation assisting or resisting the motion was presented at a fixed location along the path.
Participants performed one block of assistive and resistive trials. Hypotheses: 1) With assistive perturbations,
trajectories become less stable, exploiting the energy from the perturbation. 2) With resistive perturbations,
trajectories preceding the perturbation become more stable, reducing the chance of the ball escaping.
Contraction exponents were analytically determined for all phase space states of the cup-ball system and served
to quantify the contraction properties of the human trajectories.
Figure 1: A, B: Model of cup-and-ball task. C: Implementation in a virtual environment. D: Contraction
analysis of a perturbed trial.
Results showed that: 1) for assistive perturbations, subjects adapted time and location of perturbation onset to
be in a divergent location of phase space to exploit the assisting forces (Fig.1D); 2) for resistive perturbations,
trajectories met the perturbation in a convergent location to compensate external forces. These results
demonstrate that humans are sensitive to stability properties of the task and simplify the dynamics to make safe
interaction with objects with complex dynamics possible.
Acknowledgements: Supported by NIH R01-HD045639, NIH R01-HD087089, NSF-EAGER 1548514.
Effect of Aging on Step Adjustments to Perturbations in Visually Cued Gait Initiation
Ruopeng Sun ([email protected]), Chuyi Cui and John B. Shea
Indiana University Bloomington, Bloomington, IN, USA
Having the capability to make step adjustments in response to sudden perturbations is essential for fall
avoidance during locomotion. It requires the ability to inhibit original motor planning, select and execute
alternative motor commands in a timely manner. The incorrect strategy in body weight shifting during step
adjustments could lead to loss of balance and falls among the elderly. The present study investigated the aging
effect on step adjustments in response to a stepping-target perturbation during visually cued gait initiation. A
novel approach was used such that subject’s postural adjustments prior to swing foot lifting were analyzed in
real time and used to trigger the relocation of the stepping target. This allowed us to probe the role of postural
adjustments on the preparation and execution of a successful step, and determine the critical timing window for
making safe and successful step adjustments during perturbed walking in an aging population.
Ten healthy elderly adults (68.0 ± 4.1 years, 6 female) and ten healthy young adults (21.5 ± 1.9 years, 4 female)
were recruited to participate in this study. Subjects were asked to stand upright without shoes on a force
platform, initiate forward walking with their right foot, step on to a projected foot sized visual target located at a
step length ahead of them, and continue walking on the 5 m walkway. After the initial visual target display to
trigger subjects’ motor planning for gait initiation, the location of the visual target was either unchanged, or
randomly relocated laterally or medially by 10 cm. The relocation of the visual target disrupted the preplanned
step and triggered the online postural adjustments to select an alternative foot landing position. Three trigger
timing conditions (Early, Intermediate, Late) for target relocation were performed based on real time force
analysis of subjects’ weight distribution during the gait initiation cycle (commonly referred as the Anticipatory
Postural Adjustment, APA).
Elderly subjects showed delayed reaction time and extended double support duration for the initial step across
all test conditions. Elderly subjects also exhibited more undershoot in foot placement during the intermediate
and late target shift conditions. Furthermore, in the late target shift condition, elderly subjects rotated their foot
more prior to landing in order to step on the target, resulting in increasing difficulty in maintaining postural
stability, and increasing variability in subsequent step performance. These findings suggest that 1) older adults
have decreased ability to select and execute alternative steps under time constraint; and 2) the late effort to
make adjustment leads to higher instability and higher risk of falling.
Unilateral wrist extension training after stroke to improve bilateral function
1
Yao Sun ([email protected]) ,2 Noah Ledwell, 2Lara Boyd and 1 E. Paul Zehr
1
Rehabilitation Neuroscience Laboratory, University of Victoria, Victoria, BC, Canada
2
Brain Behaviour Laboratory, University of British Columbia, Vancouver, BC, Canada
Following stroke, muscle weakness and impaired motor function are expressed in both more (MA) and less
affected (LA) sides (1, 2). Several studies suggest resistance training improves muscular strength after stroke (3,
4). However, due to the muscular weakness, the MA limb may not be able to perform standard resistance training.
“Cross-education” describes the phenomenon that training one side of the body increases strength or motor skill
in the untrained and opposite side (5). This concept has been applied in strength training after injury in both upper
and lower limbs (6). Recently, our lab found six weeks of dorsiflexion resistance training in the LA leg improved
strength of both trained and untrained legs of stroke participants (7). This was the first study indicating “crosseducation” could be applied to enhance muscle strength in the MA leg when direct training is not feasible. To
explore if cross-education occurs also in the upper limb after stroke, participants participated in a 5-week
unilateral wrist extension training.
Twenty-two participants (> 6 moths post stroke, 65.6±6.7 years old; 12 at UVIC, 10 at UBC) were recruited for
a five-week wrist extension training intervention using the LA arm. As in prior studies, we used a multiple
baseline (3 pre-test days) design to compare training responses within subjects. Maximal voluntary contraction
(MVC) wrist extension force, and maximal muscle activation were measured for all the participants. Reciprocal
inhibition (RI) from wrist flexors to extensors, cutaneous reflexes in wrist extensor muscle from median nerve
(MED) and superficial radial nerve (SR) stimulation were tested in twelve participants. Electromyography (EMG)
of extensor carpi radialis (ECR), flexor carpi radialis (FCR), biceps brachii (BB) and triceps brachii (TB) were
recorded during all the tests. Clinical assessments included the Modified Ashworth scale, Tardieu scale, and
partial Wolf Motor Function Test and were performed by the same physical therapist.
Wrist extension MVC force increased ~51% in the trained arm and ~32% in the untrained arm, on average. Sixteen
out of twenty-two participants showed significant increases in wrist extension force in their trained LA side and
ten participants showed significant increases in the untrained MA side. Of the twelve participants who completed
the Tardieu scale, five increased wrist joint range of motion with an average of 9.8° in the direction of extension.
Significant correlations between muscle activation and size of RI were found only in the LA side before and after
training. The correlation between muscle activation and SR cutaneous reflexes was significant in both LA and
MA side after training.
This study extends to the upper limb the application of cross-education in strength training following stroke. The
results show that training less affected side could potentially facilitate wrist extension strength and function in the
more affected side. However, compared to previous study (7), the variance between participants indicate that
cross-education between upper limb strength training might not as strong as in the lower limb in stroke
participants.
References
Zehr EP, Loadman PM (2012) Persistence of locomotor-related interlimb reflex networks during walking after
stroke. Clin Neurophysiol 123:796–807. doi:10.1016/j.clinph.2011.07.049
2. Barzi Y, Zehr EP (2008) Rhythmic arm cycling suppresses hyperactive soleus H-reflex amplitude after stroke.
Clin Neurophysiol 119:1443–1452. doi:10.1016/j.clinph.2008.02.016
3. Morris, S. L., Dodd, K. J. & Morris, M. E. Outcomes of progressive resistance strength training following
stroke: a systematic review. Clin Rehabil. 18, 27ϋ39 (2004).
4. Zehr EP (2011) Evidence-based risk assessment and recommendations for physical activity clearance: stroke
and spinal cord injury. Appl Physiol Nutr Metab 36:S231
1.
Engine and Transmission: Soleus Muscle Actuator Function is Modulated by Foot Mechanics
1
Kota Z. Takahashi ([email protected]), 2Michael T. Gross, 3Herman van Werkhoven,
4
Stephen J. Piazza, and 5Gregory S. Sawicki
1
University of Nebraska at Omaha, Omaha, NE, USA, 2The University of North Carolina at Chapel Hill,
Chapel Hill, NC, USA, 3Appalachian State University, Boone, NC, USA, 4The Pennsylvania State University,
University Park, PA, USA, 5North Carolina State University, Raleigh, NC, USA
The human foot and ankle structures embody fundamental structure-function relationships that govern the way
we walk and run. For example, the ankle plantar flexor muscle-tendon group is the primary generator of
mechanical energy during locomotion [1], analogous to an engine of a vehicle. These muscle-tendon structures
operate within the inherent force-length and force-velocity properties to optimize force and work production
during push-off [2]. Yet, actuator capacity of the ankle plantar flexors may be mediated by more distal structures
in the foot. During push-off, foot structures set the leverage of the plantar flexors to modulate force-velocity
operating ranges [3], analogous to a transmission. The goal of this study was to investigate the foot-ankle
interaction and their role in regulating the mechanics and energetics of human walking.
We manipulated foot mechanics by adding
stiffness to the foot through insoles and
shoes. By adding stiffness, we manipulated
two salient functional features of the foot
during locomotion: energy dissipation and
leverage. As 20 healthy individuals walked
with a series of added foot stiffness levels,
we analyzed the resulting changes in soleus
muscle-tendon neuromechanics using
ultrasonography, electromyography, and
3D motion capture. We found that adding
stiffness to the foot increased soleus force
production (p < 0.001) and decreased
Figure 1: Added foot stiffness (ΔK) shifted the force-velocity
force velocity operating region,
region and
fascicle shortening speed (p < 0.001),
enhanced the force per unit activation (N = 20, walking at 1.25 m/s). Square brackets
indicating a shift in the force-velocity
show significant pair-wise comparisons. Five added stiffness (ΔK) levels were tested:
0 (barefoot), 14.8 ± 0.5, 22.5 ± 0.5, 28.7 ± 0.8, and 65.6 ± 2.9 N/mm.
operating ranges (Figure 1a). Furthermore,
added foot stiffness also enhanced the soleus force per unit activation (i.e., ratio of integrated force and integrated
activation) (Figure 1b, p < 0.001). Despite this economical force production, the whole-body energy expenditure
increased with greater foot/shoe stiffness (p < 0.001). This increased metabolic cost is likely due to the added
force demand on the plantar flexors, as walking on a more rigid foot/shoe surface compromises the plantar flexors’
mechanical advantage. In other words, the potential energetic benefit of the force-velocity shift may have been
counterbalanced by the increased demand to generate more force.
Our ongoing work is dedicated to ‘reverse-engineering’ the design of the ankle-foot system (i.e., engine and
transmission). Such insights will contribute to the underlying mechanisms that regulate mechanics and energetics
of locomotion, and may inspire novel designs of wearable devices (e.g., prostheses, exoskeletons, and footwear).
References
Farris and Sawicki (2012) The mechanics and energetics of human walking and running: a joint level
perspective. J R Soc Interface 9:3361-73.
2. Rubenson et al (2012) On the ascent: the soleus operating length is conserved to the ascending limb of the
force-length curve across gait mechanics in humans. J Exp Biol 20:3539-51.
3. Baxter et al (2012) Ankle joint mechanics and foot proportions differ between human sprinters and nonsprinters. Proc Biol Sci 40:386-390.
1.
Effects of aging on retention of locomotor learning
Erin V. Vasudevan ([email protected]) and 1,2Danica K. Tan
1
School of Health Technology and Management, SUNY Stony Brook University, Stony Brook, NY, USA
2
Moss Rehabilitation Research Institute, Elkins Park, PA, USA
1,2
A requirement of locomotor flexibility is the ability to adapt, or adjust movements to new demands through trialand-error practice. Since the motor system can rapidly adapt to external perturbations and then deadapt when the
perturbation is removed, it can be tempting to view adaptation as short-term learning. However, there is evidence
that memory of an adapted pattern persists for at least 24 hours [1, 2], as evidenced by faster re-learning rates or
“savings”. Here, we investigated long-term retention of a walking adaptation task. Our objectives were to
determine (1) if faster re-adaptation persists long term, (2) if, in addition to faster re-adaptation, changes in
locomotor coordination (i.e. aftereffects) can be maintained long-term, and (3) if aging affects long-term retention.
We used a well-studied split-belt treadmill adaptation task, in which two treadmill belts drive each leg at a
different speed. Baseline walking coordination was assessed in adults without neuromuscular or orthopedic
conditions (n=55, aged 18-79 years) during tied-belt walking (both belts at 0.5m/s). Subjects then adapted to splitbelts (0.5:1.0m/s) for 16 min. They returned for similar testing sessions 24 hours (“Day 2”) and 4 weeks (“1
Month”) later. The experimental paradigm is shown in Figure 1A.
We found that people across all age groups retrieved aftereffects at the beginning of Day 2 and 1 Month baseline
(tied belt) testing (Figure 1B shows data from ten 18-29 year olds and ten 50-59 year olds). However, while
younger adults re-adapted faster to splitbelts on Day 2 and 1 Month, compared to
Day 1 (Figure 1C, 18-29 year olds), older
adults did not show similar degrees of
savings (Figure 1C, 50-59 year olds).
Overall, this suggests that a memory of the
adapted pattern persists long-term, even
after only 1-2 exposures to split-belts,
since aftereffects can be retrieved when
people are placed back in the adaptation
environment regardless of age. We also
showed that savings (i.e. faster readaptation) and retention of aftereffects are
dissociable processes that are differently
affected by age. We posit that retention of
aftereffects reflects a context-dependent
memory of a specific coordination pattern,
whereas savings represents a strategy to
rapidly minimize errors in the face of a
previously-encountered perturbation.
References
1 Malone, L.A., Vasudevan, E.V., and Bastian, A.J.: ‘Motor adaptation training for faster relearning’, J Neurosci, 2011,
31, (42), pp. 15136-15143
2 Krakauer, J.W., Ghez, C., and Ghilardi, M.F.: ‘Adaptation to visuomotor transformations: consolidation, interference,
and forgetting’, J Neurosci, 2005, 25, (2), pp. 473-478
Acknowledgments: Supported by AHA #12SDG12200001 to EV
From Muscle-Tendon to Whole-Body Dynamics:
Towards a Multi-Scale Empirical Understanding of Human Movement Biomechanics
Karl E. Zelik ([email protected])
Vanderbilt University, Nashville, TN, USA
A grand challenge in the field of biomechanics is to develop a cohesive, multi-scale understanding of human
movement that links muscle-tendon, joint and whole-body dynamics. Empirical and computational methods
have been developed to estimate biomechanics at a single scale (e.g., joint work), and in some cases to bridge
between scales (trans-scale, e.g., to link muscle-tendon to joint work). However, a critical challenge remains to
overcome: biomechanical estimates at one scale often do not agree quantitatively with estimates at another. For
instance, using traditional 3D analysis methods, net mechanical work computed about the joints when a person
climbs a set of stairs overestimates the work performed to raise the center-of-mass against gravity [1]. Even for
level ground walking, mechanical work discrepancies of 25-35% have been observed [2]. Likewise, muscletendon work derived from ultrasound and force transducers may not be fully consistent with joint work
estimated from inverse dynamics. It is critical to resolve these trans-scale discrepancies in order to develop a
comprehensive, multi-scale understanding of movement. This abstract summarizes our recent progress towards
coalescing multi-scale estimates.
In one study we integrated various empirical estimates of work and energy in
order to synthesize whole-body dynamics (from Fenn, and Cavagna traditions)
with joint- and segment-level kinetics (from Braune & Fischer, and Elftman
traditions). In a second study we focused on developing and validating an EMGdriven musculoskeletal analysis to partition joint kinetics into contributions from
individual muscle-tendon units. We are now working to parse muscle fiber vs.
tendon work by integrating ultrasound with motion capture and force measures.
We demonstrated, for the first time, that joint-segment estimates could reliably
capture whole-body gait dynamics (work done on/about the center-of-mass, [1]).
We found that the key to resolving trans-scale work discrepancies was using 6
degree-of-freedom (rotational and translational) analysis of the hip, knee, ankle
and foot (Fig. 1); which revealed that the hip and foot contribute more to human
gait kinetics than conventionally estimated. Next, we demonstrated that a new
EMG-driven analysis could reproduce inverse dynamics sagittal ankle power with
high fidelity during walking (R2=0.98), while providing estimates of individual
muscle-tendon unit contributions. Future work remains to validate this approach
for different joints, activities, and additional planes. The next challenge is to parse
muscle fiber vs. tendon work. We will discuss ongoing efforts (using ultrasound)
to quantify muscle-tendon length changes and forces during movement, and to
synthesize these with our multi-scale biomechanical understanding.
Figure 1: Energy Accounting analysis links joint and segment contributions to
total energy changes of and about the center-of-mass (COM and Peripheral) [2].
References
1. Duncan et al. (1997). Gait & Posture. 5: 204-210.
2. Zelik KE, Takahashi KZ and Sawicki GS (2015). J Exp Biol. 218(6): 876-86.
Acknowledgements
This work was completed over several years, with support from NSF, DOD & Whitaker International program.
Variability and Stages of Motor Learning in Virtual and Real Environments
Zhaoran Zhang ([email protected]) and Dagmar Sternad
Virtual reality or computer-simulated games have been widely utilized in research on motor learning and in
rehabilitation of patients with neuromotor disorders [1]. Using a virtual environment (VE) has many
advantages: it guarantees tight control of experimental conditions and readily affords visual and haptic
manipulations. However, many studies only examined motor learning in VE, without comparing it to
performance of a similar task in the real environment (RE). The present study compared performance of the
same motor task in VE and RE and examined the influence of different perceptual and execution conditions on
learning and performance. To do so, we analyzed variability using a decomposition method developed by
Sternad and colleagues [2] to compare stages of learning in both VE and RE.
A redundant throwing task, skittles, has served as test bed in several previous studies on skill acquisition. A real
device was developed, replicating the exact physics of the already existent virtual game (Fig.1). In both set-ups
subjects threw a ball tethered to the post to accurately hit a target. 12 healthy subjects practiced in either VE or
RE for 6 daily sessions each. In VE, the arm was constrained to move a single-joint lever arm (Fig.1C); angle
and velocity of the lever arm were measured. Throwing of the virtual ball was controlled by lifting the index
finger off a sensor attached to a real ball fixed to the end of lever arm. In RE, subjects threw a real ball without
any constraints executing free arm movements. Kinematics of subjects’ shoulder, elbow, wrist, hand and ball
were recorded with 3D motion capture (Qualysis). Task performance was estimated by the hitting error in both
VE and RE. To quantify how subjects shaped their performance with practice, performance variability was
analyzed by decomposing data distributions into tolerance, noise, covariance components (TNC-analysis) [2].
A
B
C
D
Figure 1: (A) Real skittles game. (B) Ball and hand trajectories of real skittles in 3D space. (C) Virtual skittles
setup. (D) Top-down view of virtual skittles, as subjects see on the screen.
Results showed that in both VE and RE performance error and variability of error and hand movements
decreased, although at a different rate. TNC results on the real task were consistent with previous findings in
VE and indicated distinct stages of learning: Tolerance was optimized first, indicating exploration, followed by
covariation and noise, indicating fine-tuning of the skill. The noise component remained the highest at the end
of practice, suggesting that neuromotor noise is least accessible to practice. Replication of previous variability
findings gives important support for the generality of these insights. These findings also suggest that
performance in VE and RE share similar challenges.
References
1. Holden, M. K., & Todorov, E. (2002). Use of virtual environments in motor learning and rehabilitation.
Handbook of Virtual Environments: Design, Implementation and Applications (Ed.: K.M. Stanney),
Lawrence Erlbaum Associates, 999-1026.
2. Cohen, R. G., & Sternad, D. (2009). Variability in motor learning: relocating, channeling and reducing
noise. Experimental Brain Research, 193(1), 69-83.
How do we initiate walking gait?
Guoping Zhao ([email protected]), Sebastian Haufe, Martin Grimmer and Andre Seyfarth
Lauflabor, Technische Universität Darmstadt, Darmstadt, Germany
Maintaining balance in steady states/gaits (i.e. walking, quiet standing) and especially for transitions is important
for humans and bipedal robots. Walking gait initiation is a common gait transition in daily life. It requires 1)
propulsive forces which generate forward movement, and 2) stepping leg control which lifts the leg and puts it in
front of the center of mass (CoM) to catch up the falling. Several studies have been done to describe the
characteristics of walking initiation [1-2]. This study focuses on lower limb joint functions and underlying
mechanisms.
Walking initiation experiments with three different self-selected target speeds (slow, normal, and fast, 8
repetitions each) were conducted. Eleven young healthy subjects were instructed to stand and walk barefoot on
an instrumented walking track (6 m long, 1 m wide, 7 force plates, Kistler, Switzerland). Ground reaction force
(GRF) was recorded at 1 kHz. Full body kinematics were recorded by 10 high-speed cameras (Qualisys, Sweden)
at 500 Hz. Subjects were instructed to start with the left leg (see Fig. 1). Joint torque and power were computed
based on inverse dynamics. The CoM positions were computed by combining both kinematics and GRF. The
starting of initiation was defined as the moment when the displacement
between the center of pressure (CoP) and the CoM in walking direction
is larger than 2cm. The end of initiation is defined as the lift-off moment
of left leg. Preliminary results presented in this abstract are from four
male subjects (age 29.8±3.9 years, body mass 76.6±7.8 kg, height
1.8±0.1 m).
Target speeds were 1.00±0.09 m/s for slow, 1.48±0.15 m/s for normal,
and 2.21±0.16 m/s for high. The results (Fig. 1) show that at the
beginning of initiation the vertical force of the left leg first increases,
whereas it decreases in the right leg. This indicates that subjects try to
move the CoM to the right side while keeping the CoM vertical position
constant. Gait initiation time is almost the same for all three speeds
(start at ~0.34s, end at 1s). Both left and right ankle torques decrease at
the beginning of the initiation. For normal and fast speed, ankle torques
decrease to almost zero, which makes the movement of CoM similar to
an inverted pendulum. There is almost no power output from both
ankles before left leg lift-off. These results indicate that the lifting leg
ankle of prostheses or exoskeletons could stay passive during gait
initiation.
Figure 1: Dashed line and solid line denote left and right leg. (A) Vertical GRF normalized to body weight. (B)
Displacement between CoP and CoM in walking direction (negative means CoP behind CoM). (C) and (D) are
ankle torque and power normalized to body mass. All trials are synchronized to the lift-off of the left leg (t=1s).
References
1. Brenière Y and Do MC (1986) When and how does steady state gait movement induced from upright posture
begin? J. Biomechanics 19(12):1035-1040.
2. Brenière Y and Do MC (1991) Control of gait initiation. J Mot Behav 23(4):235–240.
Acknowledgments
This work was supported by the EU project BALANCE under Grant Agreement No. 601003.
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