Title: Performance analysis of classifiers for EMG signal of different hand movements

Authors: Raman Dev; Ajai Kumar Singh

Addresses: Electrical Engineering Department, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat, Haryana, India ' Electrical Engineering Department, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat, Haryana, India

Abstract: In the design of a health monitoring system, Electromyography (EMG) signal is one of the key parameters. So it is very important to utilise the EMG signal carefully. In this paper, different classification methods have been used to classify the EMG signals. EMG signals have been extracted from five different subjects corresponding to their eight different motions of the right hand using LabVIEW. The classification techniques used includes k-NN, naïve Bayes and Artificial Neural Network (ANN) classifiers. Five feature vectors used are mean absolute value, average band power, standard deviation, peak to peak root mean square value and root mean square value to learn the classifier. From the results obtained, it has been observed that the performance of ANN classifier in terms of classification accuracy and time required to classify is the best among the three classifiers considered for EMG signal analysis. ANN has 100% classification efficiency for classification of EMG signals obtained from different subjects relative to their hand motion. Based upon better classification efficiency, a better health monitoring system can be manufactured.

Keywords: EMG signals; electromyography; LabVIEW; naive Bayes; k-NN; k-nearest neighbour; ANNs; artificial neural networks; confusion matrix; performance evaluation; classifiers; hand movements; signal classification; hand motion; health monitoring.

DOI: 10.1504/IJBET.2016.079487

International Journal of Biomedical Engineering and Technology, 2016 Vol.22 No.3, pp.233 - 249

Received: 13 Oct 2015
Accepted: 08 Feb 2016
Published online: 29 Sep 2016 *

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