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http://dx.doi.org/10.9718/JBER.2012.33.2.065

A Gaussian Mixture Model Based Surface Electromyogram Pattern Classification Algorithm for Estimation of Wrist Motions  

Jeong, Eui-Chul (Department of Electronic Engineering, Inha University)
Yu, Song-Hyun (Department of Electronic Engineering, Inha University)
Lee, Sang-Min (Department of Electronic Engineering, Inha University)
Song, Young-Rok (Department of Electronic Engineering, Inha University)
Publication Information
Journal of Biomedical Engineering Research / v.33, no.2, 2012 , pp. 65-71 More about this Journal
Abstract
In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).
Keywords
GMM; pattern classification; EMG; feature extraction; wrist motion estimation;
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Times Cited By KSCI : 1  (Citation Analysis)
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