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A study of back-propagation and radial basis neural network on EMG signal classification

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Main Authors: Chong, Y. L., Sundaraj, Kenneth, Prof. Madya
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2010
Subjects:
Classification rates
EMG signal
Muscle movement
Radial basis neural networks
Statistical features
Voluntary movement
International Symposium on Mechatronics and its Applications (ISMA)
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/8645
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spelling my.unimap-86452010-08-13T04:38:22Z A study of back-propagation and radial basis neural network on EMG signal classification Chong, Y. L. Sundaraj, Kenneth, Prof. Madya Classification rates EMG signal Muscle movement Radial basis neural networks Statistical features Voluntary movement International Symposium on Mechatronics and its Applications (ISMA) Link to publisher's homepage at http://ieeexplore.ieee.org/ Neural networks are ubiquitous tool for classification. This paper presents a study of classifying EMG signal patterns using back-propagation and radial basis neural networks. Since the pattern of the EMG signal elicited may differ depending on the activity of the muscle movement. Therefore, the purpose of this study was to demonstrate the effectiveness of the neural networks on discriminating the patterns of certain activities to their respective category. Experiments were carried out on a selected muscle. Five subjects were asked to perform several series of voluntary movement with the respect to the muscle concerned. From the EMG data obtained, four statistical features are computed and are applied to the networks. Comparison is made based on the elements of the networks and the classification rate achieved. Generally, both networks are well performed in discriminating different EMG signal patterns with the successful rate of 88% and 89.33% respectively. 2010-08-13T04:38:22Z 2010-08-13T04:38:22Z 2009-03-23 Working Paper p.1-6 978-1-4244-3481-7 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5164797&tag=1 http://hdl.handle.net/123456789/8645 en Proceedings of the 6th International Symposium on Mechatronics and its Applications (ISMA) 2009 Institute of Electrical and Electronics Engineering (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Classification rates
EMG signal
Muscle movement
Radial basis neural networks
Statistical features
Voluntary movement
International Symposium on Mechatronics and its Applications (ISMA)
spellingShingle Classification rates
EMG signal
Muscle movement
Radial basis neural networks
Statistical features
Voluntary movement
International Symposium on Mechatronics and its Applications (ISMA)
Chong, Y. L.
Sundaraj, Kenneth, Prof. Madya
A study of back-propagation and radial basis neural network on EMG signal classification
description Link to publisher's homepage at http://ieeexplore.ieee.org/
format Working Paper
author Chong, Y. L.
Sundaraj, Kenneth, Prof. Madya
author_facet Chong, Y. L.
Sundaraj, Kenneth, Prof. Madya
author_sort Chong, Y. L.
title A study of back-propagation and radial basis neural network on EMG signal classification
title_short A study of back-propagation and radial basis neural network on EMG signal classification
title_full A study of back-propagation and radial basis neural network on EMG signal classification
title_fullStr A study of back-propagation and radial basis neural network on EMG signal classification
title_full_unstemmed A study of back-propagation and radial basis neural network on EMG signal classification
title_sort study of back-propagation and radial basis neural network on emg signal classification
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2010
url http://dspace.unimap.edu.my/xmlui/handle/123456789/8645
_version_ 1643789245394976768
score 13.222552

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