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The Use of Artificial Neural Network in the Classification of EMG Signals

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3 Author(s)
Ahsan, M.R. ; Dept. of Electr. & Comput. Eng., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia ; Ibrahimy, M.I. ; Khalifa, O.O.

This paper presents the design, optimization and performance evaluation of artificial neural network for the efficient classification of Electromyography (EMG) signals. The EMG signals are collected for different types of volunteer hand motion which are processed to extract some predefined features as inputs to the neural network. The time and time-frequency based extracted feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been employed for the classification of EMG signals. The results show that the designed and optimized network able to classify single channel EMG signals with an average success rate of 88.4%.

Published in:

Mobile, Ubiquitous, and Intelligent Computing (MUSIC), 2012 Third FTRA International Conference on

Date of Conference:

26-28 June 2012