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dc.contributor.authorChong, Y. L.
dc.contributor.authorSundaraj, Kenneth, Prof. Madya
dc.date.accessioned2010-08-13T04:38:22Z
dc.date.available2010-08-13T04:38:22Z
dc.date.issued2009-03-23
dc.identifier.citationp.1-6en_US
dc.identifier.isbn978-1-4244-3481-7
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5164797&tag=1
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/8645
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.org/en_US
dc.description.abstractNeural 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.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineering (IEEE)en_US
dc.relation.ispartofseriesProceedings of the 6th International Symposium on Mechatronics and its Applications (ISMA) 2009en_US
dc.subjectClassification ratesen_US
dc.subjectEMG signalen_US
dc.subjectMuscle movementen_US
dc.subjectRadial basis neural networksen_US
dc.subjectStatistical featuresen_US
dc.subjectVoluntary movementen_US
dc.subjectInternational Symposium on Mechatronics and its Applications (ISMA)en_US
dc.titleA study of back-propagation and radial basis neural network on EMG signal classificationen_US
dc.typeWorking Paperen_US


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