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

• Autorzy: Negi Sachin , Kumar Yatindra , Mishra V. M

Identyfikatory

DOI

10.15439/2017R96

• Konferencja: The Second International Conference on Research in Intelligent and Computing in Engineering
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

sEMG; MLPNN; kNN; upper limb prosthesis

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2017
• Tom: Vol. 10
• Strony: 269--292
• Opis fizyczny: Bibliogr. 16 poz., fot., tab., wykr.

Twórcy

autor

Negi Sachin

• Dept. of Electrical Engineering G. B. Pant Engineering College Pauri- Garhwal, Uttarakhand, India

autor

Kumar Yatindra

• Dept. of Electrical Engineering G. B. Pant Engineering College Pauri- Garhwal, Uttarakhand, India

autor

Mishra V. M

• Dept. of Electrical Engineering G. B. Pant Engineering College Pauri- Garhwal, Uttarakhand, India

Bibliografia

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