Prosthesis Control Using a Nearest Neighbor Electromyographic Pattern Classifier

1 June 1983

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering

Vol. 30 (6) , 356-360
https://doi.org/10.1109/tbme.1983.325138

Abstract

An investigation was conducted into the feasibility of applying a nearest neighbor algorithm to the problem of recognizing electromyographic (EMG) signal patterns for prosthesis control. A nearest neighbor algorithm correctly identified arm motions as belonging to one of six pattern classes from 72 to 100 percent of the time. A condensed nearest neighbor classifier was constructed to determine what minimum number of vectors was necessary in the look-up table.

Keywords

This publication has 3 references indexed in Scilit:

Clinical application study of multifunctional prosthetic hands
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Two Modifications of CNN
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The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory, 1968