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Annals of Computer Science and Information Systems, Volume 10

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

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

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DOI: http://dx.doi.org/10.15439/2017R96

Citation: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, Vijender Kumar Solanki, Vijay Bhasker Semwal, Rubén González Crespo, Vishwanath Bijalwan (eds). ACSIS, Vol. 10, pages 269–272 ()

Full text

Abstract. 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.

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