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Article: Fuzzy EMG classification for prosthesis control

TitleFuzzy EMG classification for prosthesis control
Authors
Issue Date2000
PublisherIEEE.
Citation
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000, v. 8 n. 3, p. 305-311 How to Cite?
AbstractThis paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.
Persistent Identifierhttp://hdl.handle.net/10722/42858
ISSN
2015 Impact Factor: 2.583
2015 SCImago Journal Rankings: 1.385
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorYang, YSen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorZhang, YTen_HK
dc.contributor.authorParker, PAen_HK
dc.date.accessioned2007-03-23T04:33:32Z-
dc.date.available2007-03-23T04:33:32Z-
dc.date.issued2000en_HK
dc.identifier.citationIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000, v. 8 n. 3, p. 305-311en_HK
dc.identifier.issn1534-4320en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42858-
dc.description.abstractThis paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.en_HK
dc.format.extent193489 bytes-
dc.format.extent26624 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshArtificial limbsen_HK
dc.subject.meshElectric stimulation therapy - methodsen_HK
dc.subject.meshElectromyography - classification - methodsen_HK
dc.subject.meshNeural networks (computer)en_HK
dc.titleFuzzy EMG classification for prosthesis controlen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1534-4320&volume=8&issue=3&spage=305&epage=311&date=2000&atitle=Fuzzy+EMG+classification+for+prosthesis+controlen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/86.867872en_HK
dc.identifier.pmid11001510-
dc.identifier.scopuseid_2-s2.0-0034283513-
dc.identifier.hkuros58218-
dc.identifier.isiWOS:000089336700005-
dc.identifier.citeulike5923546-

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