Journal of Rehabilitation Research & Development (JRRD)

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Volume 48 Number 6, 2011
   Pages 739 — 754

Abstract —  Electromyogram-based neural network control of transhumeral prostheses

Christopher L. Pulliam, MS;1* Joris M. Lambrecht, MS;1 Robert F. Kirsch, PhD1-2

1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH; 2Center of Excellence in Functional Electrical Stimulation, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH

Abstract — 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 15.7° and 24.9° and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.

Key words: amputation, artificial neural network, control, electromyographic, myoelectric, myoelectric control, pattern recognition, prosthesis, prosthetic limb, transhumeral.


View HTML  ¦  View PDF  ¦  Contents Vol. 48, No. 6
This article and any supplementary material should be cited as follows:
Pulliam CL, Lambrecht JM, Kirsch RF. Electromyogram-based neural network control of transhumeral prostheses. J Rehabil Res Dev. 2011;48(6):739-54.
DOI:10.1682/JRRD.2010.12.0237
Crossref

Last Reviewed or Updated  Monday, July 11, 2011 9:24 AM

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