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Restoring Voluntary Function in Artificial Limbs Todd A. Kuiken, MD, PhD Neural Engineering Center for Artificial Limbs Rehabilitation Institute of Chicago.

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Presentation on theme: "Restoring Voluntary Function in Artificial Limbs Todd A. Kuiken, MD, PhD Neural Engineering Center for Artificial Limbs Rehabilitation Institute of Chicago."— Presentation transcript:

1 Restoring Voluntary Function in Artificial Limbs Todd A. Kuiken, MD, PhD Neural Engineering Center for Artificial Limbs Rehabilitation Institute of Chicago Department of PM&R, BME and Surgery, Northwestern University May 2010 Grand Challenges in Neural Engineering

2 Body-Powered Prostheses Developed in the Civil War – refined in WWII Moving shoulders forward pulls on a bicycle cable Bicycle cable operates hook or hand and elbow

3 Myoelectric Prostheses “Myo” - muscle When 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.

4 We Need a Neural Interface… To acquire motor control data To stimulate the afferent system Options: –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…

5 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

6 Motion During Contractions

7 Blocks and Box Test Original Prosthesis (Used more than 20 months) Nerve Transfer Prosthesis (Used about 2 months)

8 Blocks and Box Test Original ProsthesisNerve Transfer Prosthesis (Used more than 8 months)(Used about 2 months)

9 Sensory Reinnervation Studies Jesse Sullivan Sensory Map Paul Marasco

10 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 skin Creates a portal to sensory pathways POTENTIAL ADVANTAGES –Provides physiologically appropriate sensory feedback –Provides anatomically appropriate sensory feedback

11 Targeted Reinnervation Functional Outcomes Functional Outcomes of 1 st six patients times faster on Block and Box test 50% faster on Clothes Pin test Improvement in speed on all Wolf Motor Functions tests Significant improvement in AMPs testing Transfer sensation in four patients One Unsuccessful Transhumeral Surgery In OR, radial nerve atrophy discovered Likely brachial plexopathy patients worldwide Vienna University of Washington Walter Reed Army Medical Center Brook Army Medical Center Edmonton, Canada 96% Surgical success rate in producing usable EMG signals University of Alberta TMR subject Two prosthetic arms systems commercially available Liberating Technology—Boston Elbow Otto Bock—TMR Dynamic Arm

12 Advanced Signal Processing Techniques Kevin Englehart UNB, BME 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 Electrodes SubjectTime Domain AR+RMS BSD*98.4± ±1.1 STH**90.3± ±2.9 LTH LTH Average96.0± ±5.2 * average of 3 experiments and 3 different bipolar electrode configurations ** average of 2 experiments and 3 different bipolar electrode configurations

13 How Many Electrodes Do We Need? Courtesy of JHU-APL and RIC Classification Accuracy (%) Number of Bipolar Electrodes 16 classes –2 elbow –4 wrist –10 hand Electrode Channel Reduction Analysis

14 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 problem Closer to the source, generally the better the signal separation Richness of Neural Interface

15 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

16 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

17 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

18 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 promise Sensory substitution does not work –Can’t rely on ‘neural plasticity’ too much –Need physiologically and anatomically appropriate feedback Need Multidimensional Sensation Feedback

19 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

20 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 Solutions Osseointegration (direct skeletal attachment) is very promising for prosthetics From

21 Grand Challenges in Neural Engineering Many of our technologies will be seeing deployment in humans for the first time soon People with severe traumatic disabilities have high incident of psychological difficulties –Depression, PTSD, Anxiety disorders, adjustment disorders Recommend careful psychological screening Recommend 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

22 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 #1K08HD A1 –Grant # R01 HD –Grant # R01 HD –Grant # NO1-HD Defense Advanced Research Projects Agency The National Institute of Disability and Rehabilitation Research US Army Generous Philanthropic Support


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