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The use of artificial neural network in the classification of EMG signals

Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2012) The use of artificial neural network in the classification of EMG signals. In: The 3rd FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (MUSIC '12), 26-28 June 2012, Vancouver, Canada.

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Abstract

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 timefrequency 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%.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 4637/25965
Uncontrolled Keywords: Electromyography, Artificial Neural Network, Back-Propagation, Levenberg-Marquardt algorithm, EMG Signal Classifier etc.
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering
Depositing User: Dr Muhammad Ibrahimy
Date Deposited: 21 Nov 2012 10:14
Last Modified: 21 Jan 2013 13:29
URI: http://irep.iium.edu.my/id/eprint/25965

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