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

Negi Sachin, Kumar Yatindra, Mishra V. M

Annals of Computer Science and Information Systems

2017

Vol. 10

269--292

Analysis 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 MLPNN with back propagation algorithm. Using MLPNN classifier, an average classification accuracy of 93.71\% was achieved over ten subjects for the combination of [MAV1, WL, AAC, ZC, and WAMP] features. Next the classification accuracy is obtained with kNN classifier for k= 3, 5, and 7. The results showed that average classification accuracy of 93.06\% is achieved using kNN and it is better than MLPNN in terms of time and simplicity.