Restoring Voluntary Function in Artificial Limbs
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Transcript of Restoring Voluntary Function in Artificial Limbs
Restoring Voluntary Function in Artificial Limbs
Todd A. Kuiken, MD, PhD
Neural Engineering Center for Artificial LimbsRehabilitation Institute of Chicago
Department of PM&R, BME and Surgery, Northwestern UniversityMay 2010
Grand Challenges in Neural Engineering
Body-Powered Prostheses
Developed in the Civil War – refined in WWIIMoving shoulders forward pulls on a bicycle cableBicycle cable operates hook or hand and elbow
Myoelectric Prostheses
“Myo” - muscleWhen muscles contract, they generate electric signals called “myoelectric signals”Electrodes on the skin over muscles can pick up these signals. The signals are then used to tell a motorized arm what to do.
We Need a Neural Interface…To acquire motor control dataTo stimulate the afferent systemOptions:– EMG from residual limbs
• current standard• Limited data available with high level amputations
– Direct peripheral nerve recording• Compelling method to get both motor control data and be able to
stimulate afferents. Exciting demonstrations in humans• Technically very challenging—not seen clinical deployment yet
– Spinal Cord– Brain machine interfacing– Targeted Reinnervation
• A pragmatic use of muscle as a biological amplifier of peripheral nerve motor signals and portal to cutaneous sensation
We Need a Neural Interface…
Targeted Muscle Reinnervation
TECHNIQUE– Residual nerves transferred to spare muscle
and skin.– Muscle acts as a ‘biological amplifier’ of the
motor command
ADVANTAGES– Additional control signals for
simultaneous control of more degrees-of-freedom
– Control signals are physiologically appropriate
• More natural feel• Easier, more intuitive
operation– Shoulder still available for
controlling other functions– No implanted hardware
required– Can use existing
myoelectric prosthetic technology
DISADVANTAGE– Requires additional surgery
• unless it is done at time of amputation
Motion During Contractions
Blocks and Box Test
Original Prosthesis(Used more than 20 months)
Nerve Transfer Prosthesis(Used about 2 months)
Blocks and Box Test
Original Prosthesis Nerve Transfer Prosthesis(Used more than 8 months) (Used about 2 months)
Sensory Reinnervation StudiesJesse Sullivan Sensory Map
Paul Marasco
Targeted Sensory Reinnervation
TECHNIQUE– Denervate residual limb skin to allow the hand
afferents to reinnervate this skin– Stimuli detected by sensors in prosthetic hand can be
applied to reinnervated skinCreates a portal to sensory pathwaysPOTENTIAL ADVANTAGES– Provides physiologically appropriate sensory
feedback– Provides anatomically appropriate sensory feedback
CONTROLLER
TOUCH SENSORS
TACTOR
Targeted Reinnervation Functional Outcomes
Functional Outcomes of 1st six patients
2.5-7 times faster on Block and Box test50% faster on Clothes Pin test Improvement in speed on all Wolf Motor Functions testsSignificant improvement in AMPs testing
Transfer sensation in four patients
One Unsuccessful Transhumeral Surgery
In OR, radial nerve atrophy discoveredLikely brachial plexopathy
40-50 patients worldwideViennaUniversity of WashingtonWalter Reed Army Medical CenterBrook Army Medical CenterEdmonton, Canada
96% Surgical success rate in producing usable EMG signals
University of Alberta TMR subject
Two prosthetic arms systems commercially availableLiberating Technology—Boston Elbow
Otto Bock—TMR Dynamic Arm
Advanced Signal Processing Techniques
Kevin EnglehartUNB, BME
thumb abduction
thumb adduction
wrist supination
wrist pronation
elbow flexion
elbow extension
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mV
Pattern Recognition Results Linear Discriminant Analysis (LDA) with time domain feature sets and a combination of autoregressive features and the root mean square (AR+RMS) feature sets were used.
Bipolar ElectrodesSubject Time
DomainAR+RMS
BSD* 98.4±0.7 97.8±1.1
STH** 90.3±2.9 87.6±2.9
LTH1 97.1 95.5
LTH2 98.3 99.2
Average 96.0±3.9 95.0±5.2* average of 3 experiments and 3 different bipolar
electrode configurations** average of 2 experiments and 3 different bipolar
electrode configurations
How Many Electrodes Do We Need?
Courtesy of JHU-APL and RIC
Cla
ssifi
catio
n A
ccur
acy
(%)
P1P2P3P4
100 90
60
80 70
50 40 30 20 10 0
1 2 3 4 5 6 7 8 9 10 11 12 … >300
Number of Bipolar Electrodes
16 classes– 2 elbow– 4 wrist– 10 hand
Electrode Channel Reduction Analysis
Grand Challenges in Neural Engineering
We want as much motor control data as possible– Need lots to control more
degrees of freedom– Need separable data to control
multiple DOFs simultaneously
Back to source separation problemCloser to the source, generally the better the signal separation
Richness of Neural Interface
Grand Challenges in Neural Engineering
Need to decode of signals robustly– Extract as much info as possible– Need to ‘learn’ the patient and the
task
Constant tension between ‘smart’ devices and human control– Example: slip sensors
Potential solutions• Better ‘information fusion”• Consider time-history systems• Adaptive algorithms
Smart Decoding and Control Algorithms
Grand Challenges in Neural Engineering
Signal Stability– Surface EMG signals are
problematic• Location different each
time prosthesis is donned
• Electrodes shift with prosthetic use
– Potential solutions• Developing new
surface EMG interfaces• Hoping for implantable
EMG system
Grand Challenges in Neural Engineering
Amputees are very active– System needs to withstand repetitive
deformation• Prosthetic sockets dig in
– System needs to withstand high force impacts
– The flying kid test
Potential solutions– External devices are easier
• Replaceable• Can incase in socket
– Internal devices:– Need to be small and tough– And/Or they need to be
compliant like tissues
Robustness of Neural Interface
Grand Challenges in Neural Engineering
TR can provide some cutaneous feedback– Not enough room for electrodes and tactors– Can’t control reinnervation process
Proprioception necessary for complex limb system– TR can’t provide proprioception– Proprioception poorly understood– Direct nerve, spinal cord and cortical
stimulation hold more promiseSensory substitution does not work – Can’t rely on ‘neural plasticity’ too much– Need physiologically and anatomically
appropriate feedback
Need Multidimensional Sensation Feedback
Need lighter devices for amputees– This is what patients complain about!
Need more robust devices– They breakdown all the time…
Need more dexterous devices– As we develop the ability to control more
DOF’s, we need more dexterous devices– Functionally, multi-degree-of freedom wrists
are particular important
Grand Challenges in Neural Engineering
Mechatronics Challenges
Grand Challenges in Neural Engineering
Need better attachment systems – Stability of control and
mechatronics depends on mechanical fixation
– Powered orthotics are equally (or more) challenging
Potential SolutionsOsseointegration (direct skeletal attachment) is very promising for prosthetics
From http://www.branemark.se/osseointegration.htm
Grand Challenges in Neural Engineering
Many of our technologies will be seeing deployment in humans for the first time soonPeople with severe traumatic disabilities have high incident of psychological difficulties– Depression, PTSD, Anxiety disorders, adjustment disorders
Recommend careful psychological screeningRecommend no media until a trial is finished and successful– Having media follow a new patient puts too much pressure on
the patient and the clinical team– Of course, disclose problems/failures with patients’ identity
protected at meetings, in papers and all reports.
Psychological Challenges for Patient with Disabilities
Collaborators and Support
NECAL Team– Todd Kuiken, MD, PhD– Aimee Schultz, MS– Blair Lock, MS– Bob Parks, MBA– Dat Tran, BS– Kathy Stubblefield, OTR– Laura Miller, CP, PhD– Levi Hargrove, PhD– Robert Lipschutz, CP– Jon Sensinger, PhD
Northwestern University– Gregory Dumanian, MD– Richard Weir, PhD– Jules Dewald, PhD
Previous Post Docs– Nikolay Stoykov, PhD– Madeleine Lowery, PhD– Ping Zhou, PhD– Helen Huang, PhD– Paul Marasco, PhD
Collaborating Institution– University of New
Brunswick– Liberating Technologies
Inc– Otto Bock, Inc.– Deka Research, Inc– Johns Hopkins Applied
Physics Lab– Kinea Design
This work supported by:The National Institutes of Health– Grant #1K08HD01224-01A1– Grant # R01 HD043137-01 – Grant # R01 HD044798-01 – Grant # NO1-HD-5-3402
Defense Advanced Research Projects AgencyThe National Institute of Disability and Rehabilitation ResearchUS ArmyGenerous Philanthropic Support