Comparison study of muscular-contraction classification between independent component analysis and artificial neural network

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dc.contributor.authorSueaseenak, Direk
dc.contributor.authorWibirama, Sunu
dc.contributor.authorChanwimalueang, Theerasak
dc.contributor.authorPintavirooj, Chuchart
dc.contributor.authorSangworasil, Manus
dc.date.accessioned2026-08-06T09:57:20Z
dc.date.available2026-08-06T09:57:20Z
dc.date.issued2008-12-01
dc.description.abstractWe developed a multi-channel electromyogram acquisition system using PSOC microcontroller to acquire multichannel EMG signals. An array of 4 x 4 surface electrodes was used to record the EMG signal. The obtained signals were classified by a back-propagation-type artificial neural network. B-spline interpolation technique has been utilized to map the EMG signal on the muscle surface. The topological mapping of the EMG is then analyzed to classify the pattern of muscle contraction using independent component analysis. The proposed system was successfully demonstrated to record EMG data and its surface mapping. The comparison study of muscular contraction classification using independent component analysis and artificial neural network demonstrates shows that performance of ANN classification is as comparable as that of the ICA. The computational time of ANN is also less than that of the ICA. © 2008 IEEE.
dc.identifier.citation2008 International Symposium on Communications and Information Technologies Iscit 2008, 468-472, 2008
dc.identifier.doi10.1109/ISCIT.2008.4700236
dc.identifier.other2-s2.0-67549107947
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/2217
dc.source2008 International Symposium on Communications and Information Technologies Iscit 2008
dc.subjectANN
dc.subjectEMG
dc.subjectICA
dc.subjectPCA
dc.titleComparison study of muscular-contraction classification between independent component analysis and artificial neural network
dc.typeConference Paper

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