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EMG motion pattern classification through design and optimization of neural network

Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2012) EMG motion pattern classification through design and optimization of neural network. In: 2012 International Conference on Biomedical Engineering (ICoBE), 27-28 February, 2012, Penang, Malaysia.

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Abstract

This paper illustrates the classification of EMG signals through design and optimization of Artificial Neural Network (ANN). Different types of ANN models are basically structured with many interconnected network elements which can develop pattern classification strategies based on a set of input/training data. The ANN models work in parallel thus providing higher computational performance than traditional classifiers which function sequentially. The EMG signals obtained for different kinds of hand motions, which further denoised and processed to extract the features. Extracted time and time-frequency based feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The results show that the designed network is optimized for 10 hidden neurons with 7 input features and able to efficiently classify single channel EMG signals with an average success rate of 88.4%.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 4637/25201
Uncontrolled Keywords: EMG signal, neural network, EMG motion pattern, EMG signal classification
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Dr Muhammad Ibrahimy
Date Deposited: 30 Aug 2012 16:08
Last Modified: 30 Aug 2012 16:08
URI: http://irep.iium.edu.my/id/eprint/25201

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