We propose to provide a set of clinically viable tools and procedures to substantially improve the control of myoelectric prostheses by transradial amputees. This proposal includes a pattern-recognition control package based on algorithms with demonstrated efficacy, an innovative new surgical technique for patients with transradial amputation that will substantially improve their ability to control prostheses and receive sensation feedback from them, and advanced pattern-recognition tools that will lead to even more robust adaptive control. In this project we will perform a series of tests that will improve the control of transradial prostheses using pattern recognition techniques. Subjects will be allowed to take these prostheses home for a month-long trial, to observe and rectify any remaining problems. Targeted Muscle Reinnervation (TMR) will be performed on six subjects. TMR is a new surgical technique that transfers amputated nerve to spare muscle and skin. It provides new myoelectric signals allowing intuitive and simultaneous control for improved function in amputees TMR will improve the accuracy of control, the number of classes that may be robustly controlled, and potentially allow for simultaneous, independent control of the hand and wrist. TMR allows for more natural control of the prosthesis, and also provides targeted sensory feedback, in which the subject feels their amputated hand through reinnervated skin on the residual limb. These two surgical procedures will greatly improve the function of transradial prostheses. TMR subjects will also undergo a field trial. Finally, adaptive pattern recognition techniques and parallel classifier technology will be investigated. Adaptive control may be crucial to clinical robustness from day to day as the user adapts to the classifier. Parallel control will allow subjects to simultaneously control wrist and hand classes, with high accuracy. This proposal will advance several areas of science, including pattern recognition and the physiology of reinnervation. Parallel classifiers and adaptive algorithm theory will be substantially developed beyond the current state of the art, and the concept of robustness in the absence of a known class will be explored in the context of electromyographic signals. This proposal will also advance our understanding of motor control as we implement these novel control techniques, and provide support for future experiments which will further develop our understanding in both motor and sensory reinnervation. An outstanding team has been assembled including the Rehabilitation Institute of Chicago, the University of New Brunswick, and Otto Bock, Inc. We believe the proposed research will advance the standard of care of persons with amputation. It will also serve as an important research platform for continuing to improve artificial limb function.
. This project will apply an innovative surgery technique to subjects with transradial amputation, to improve control of their prosthesis. It will also develop new technologies to advance the control of prostheses that have more functions including wrist rotation, wrist flexion/extension and hands with moving fingers and thumbs. These studies will significantly improve the function of artificial arms for people with below elbow amputations.
|Scheme, Erik; Englehart, Kevin (2013) Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control. J Prosthet Orthot 25:76-83|
|Wurth, Sophie M; Hargrove, Levi J (2013) Real-time comparison of conventional direct control and pattern recognition myoelectric control in a two-dimensional Fitts' law style test. Conf Proc IEEE Eng Med Biol Soc 2013:3630-3|
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|Kryger, Michael; Schultz, Aimee E; Kuiken, Todd (2011) Pattern recognition control of multifunction myoelectric prostheses by patients with congenital transradial limb defects: a preliminary study. Prosthet Orthot Int 35:395-401|
|Scheme, Erik; Englehart, Kevin (2011) Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev 48:643-59|
|Scheme, E; Biron, K; Englehart, K (2011) Improving myoelectric pattern recognition positional robustness using advanced training protocols. Conf Proc IEEE Eng Med Biol Soc 2011:4828-31|
|Simon, Ann M; Hargrove, Levi J; Lock, Blair A et al. (2011) Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. J Rehabil Res Dev 48:619-27|
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