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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30

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45

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55

60

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75

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