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In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classificationis proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wristmotions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris andextensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature isderived from extracted EMG signals in time domain for fast processing. The estimated features based in differenceabsolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach isevaluated by recognition rates and it is found that the proposed GMM-based method yields better results than con-ventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Dis-criminant Analysis(LDA).Key words: GMM, pattern classification, EMG, feature extraction, wrist motion estimation

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