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A Gaussian Mixture Model Based Pattern Classification Algorithm of Forearm Electromyogram  

Song, Y.R. (인하대학교 정보전자공동연구소)
Kim, S.J. (인하대학교 전자공학과)
Jeong, E.C. (인하대학교 전자공학과)
Lee, S.M. (인하대학교 전자공학과)
Publication Information
Journal of rehabilitation welfare engineering & assistive technology / v.5, no.1, 2011 , pp. 95-101 More about this Journal
Abstract
In this paper, we propose the gaussian mixture model based pattern classification algorithm of forearm electromyogram. We define the motion of 1-degree of freedom as holding and unfolding hand considering a daily life for patient with prosthetic hand. For the extraction of precise features from the EMG signals, we use the difference absolute mean value(DAMV) and the mean absolute value(MAV) to consider amplitude characteristic of EMG signals. We also propose the D_DAMV and D_MAV in order to classify the amplitude characteristic of EMG signals more precisely. In this paper, we implemented a test targeting four adult male and identified the accuracy of EMG pattern classification of two motions which are holding and unfolding hand.
Keywords
EMG; Gaussian mixture model; pattern classification; Feature extraction; D_DAMV; D_MAV;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
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