%0 Journal Article
%T Electromyogram-based neural network control of transhumeral prostheses
%A Christopher L. Pulliam
%A MS
%A Joris M. Lambrecht
%A MS
%A Robert F. Kirsch
%A PhD
%J Journal of Rehabilitation Research and Development
%D 2011
%I Rehabilitation Research and Development Service, Department of Veterans Affairs
%X Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG) recording system with a wireless telemetry system that communicates with the patient's prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination) based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7กใ and 24.9กใ and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.
%K amputation
%K artificial neural network
%K control
%K electromyographic
%K myoelectric
%K myoelectric control
%K pattern recognition
%K prosthesis
%K prosthetic limb
%K transhumeral
%U http://www.rehab.research.va.gov/jour/11/486/pdf/pulliam486.pdf