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iAnalysis of sEMG signal has been an emerging field for the myoelectric control of upper limb prosthesis. The objective of present work is to obtain the performance measures like accuracy, sensitivity, specificity and positive predictivity using MLMNN with back propagation algorithm. Using MLMNN classifier, an average classification accuracy of 93.71% was achieved over ten subjects for the combination of [MAV1, WL, AAC, ZC, and WAMM] features. Next the classification accuracy is obtained with kNN classifier for ky 3, 5, and 7. The results showed that average classification accuracy of 93.06% is achieved using kNN and it is better than MLMNN in terms of time and simplicity.

MLPNN and kNN Based Classification of sEMG Signal for Myoelectric Control of Upper Limb Prosthesis