A New Approach To Amputee Training Using Computer Graphics

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1995

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Abstract

In this paper, a new approach is presented for the training of amputees for an effective fitting of upper-extremity myo-electric prostheses, Electromyogram (EMG) signals from the remaining muscles of the stump are amplified and filtered, digitized with 8 bit resolution and fed htro a microcomputer with a sampling rate of 2500 Hz, The extracted feature is the integral absolute value of the biceps and triceps. A fuzzy classifier is used for clustering and classification. The EMG signals are processed and the decision of the classifier animates a graphically simulated 3 degree of freedom prosthesis on the microcomputer monitor. Five healthy persons have been trained with this system It took less than 10 minutes for the subjects to familiarize with the operation of the system. The error rate was less than 5%. The main advantages of such an approach are thought to be: training of amputees before using a real prosthesis by remembering them the concept of "muscle state", easy evaluation of the misclassification error rate of different algorithms, expert man-power time saving, accurate follow-up of the amputee, more availability of the training set and self-paced learning so less frustration of the amputee.

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From "MEC 95," Proceedings of the 1995 MyoElectric Controls/Powered Prosthetics Symposium Fredericton, New Brunswick, Canada: August, 1995. Copyright University of New Brunswick.

Copyright 2002, 2005 and 2008, The University of New Brunswick.

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License. Creative Commons License