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Cited 21 time in webofscience Cited 18 time in scopus
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dc.contributor.authorPARK, SEONG SIK-
dc.contributor.authorCHUNG, WAN KYUN-
dc.contributor.authorKIM, KEE HOON-
dc.date.accessioned2019-12-04T15:30:12Z-
dc.date.available2019-12-04T15:30:12Z-
dc.date.created2019-11-14-
dc.date.issued2020-06-
dc.identifier.issn0018-9294-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/100300-
dc.description.abstractA direct, ready-to-use surface electromyogram (sEMG) pattern classification algorithm that does not require prerequisite training, regardless of the user, is proposed herein. In addition to data collection, conventional supervised learning approaches for sEMG require labeling and segmenting the data and additional time for the learning algorithm. Consequently, these approaches cannot cope well with sEMG patterns during motion transitions of various movement speeds. The proposed unsupervised and self-adaptive method employs an iterative self-adaptive procedure realized by the probabilistic methods of diffusion, updating, and registration to cluster the activation patterns simultaneously in real time, and classify the current sEMG as new clustered patterns. Experiments demonstrated that even for the same motion, the proposed method could autonomously detect changes in muscular activation patterns varying with the speed of motion. Furthermore, some patterns of both steady- and transient-state motions could be distinguished. In addition, it was verified that the classified sEMG pattern could be correlated consistently with the actual motion, thereby realizing a high level of motion classification.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.relation.isPartOfIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING-
dc.titleTraining-Free Bayesian Self-Adaptive Classification for sEMG Pattern Recognition Including Motion Transition-
dc.typeArticle-
dc.identifier.doi10.1109/TBME.2019.2947089-
dc.type.rimsART-
dc.identifier.bibliographicCitationIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.67, no.6, pp.1775 - 1786-
dc.identifier.wosid000537293200024-
dc.citation.endPage1786-
dc.citation.number6-
dc.citation.startPage1775-
dc.citation.titleIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING-
dc.citation.volume67-
dc.contributor.affiliatedAuthorPARK, SEONG SIK-
dc.contributor.affiliatedAuthorCHUNG, WAN KYUN-
dc.contributor.affiliatedAuthorKIM, KEE HOON-
dc.identifier.scopusid2-s2.0-85077800713-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.type.docTypeArticle-
dc.subject.keywordPlusFRAMEWORK-
dc.subject.keywordPlusSIGNALS-
dc.subject.keywordAuthorSupervised learning-
dc.subject.keywordAuthorBiomedical measurement-
dc.subject.keywordAuthorMuscles-
dc.subject.keywordAuthorTransient analysis-
dc.subject.keywordAuthorMaximum likelihood estimation-
dc.subject.keywordAuthorBayes methods-
dc.subject.keywordAuthorTraining-
dc.subject.keywordAuthorAdaptive learning-
dc.subject.keywordAuthorBayesian method-
dc.subject.keywordAuthorpattern classification-
dc.subject.keywordAuthorsurface electromyogram (sEMG)-
dc.subject.keywordAuthorunsupervised probabilistic method-
dc.relation.journalWebOfScienceCategoryEngineering, Biomedical-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-

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정완균CHUNG, WAN KYUN
Dept of Mechanical Enginrg
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