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Cited 2 time in webofscience Cited 4 time in scopus
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dc.contributor.authorYun-
dc.contributor.authorJeung-
dc.contributor.authorSong-
dc.contributor.authorCHUNG, YOONYOUNG-
dc.date.accessioned2020-12-08T08:51:28Z-
dc.date.available2020-12-08T08:51:28Z-
dc.date.created2020-10-29-
dc.date.issued2020-10-
dc.identifier.issn2169-3536-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/104544-
dc.description.abstractMuscle fatigue is required to be assessed in real-time to maintain the best physical condition, especially for sports and rehabilitation areas. In recent years, numerous studies proposed muscle fatigue estimation methods with non-invasive surface electromyography (sEMG). However, the previous approaches were limited to discerning whether muscle fatigue occurs and were unable to quantify the fatigue level due to individual differences in muscle characteristics. In this study, we propose a novel method for quantitative muscle fatigue estimation that is applicable for various people without individual calibration. Because muscle mass is closely related to muscular endurance, it is utilized as a standard parameter in our assessment process. We introduce a new concept of muscle fatigue score (MFS), based on the cosine similarity between muscle mass and representative fatigue indicators. The MFS exhibits a high correlation coefficient ( jR j = 0 :7398) with key muscle characteristics compared to previous representative muscle fatigue indicators calculated from sEMG: mean frequency ( jR j = 0 :2848), median frequency ( jR j = 0 :1972), and low-frequency ratio R j = 0 :0346).-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.relation.isPartOfIEEE ACCESS-
dc.titleNon-Invasive Quantitative Muscle Fatigue Estimation Based on Correlation Between sEMG Signal and Muscle Mass-
dc.typeArticle-
dc.identifier.doi10.1109/ACCESS.2020.3029792-
dc.type.rimsART-
dc.identifier.bibliographicCitationIEEE ACCESS, v.8, pp.191751 - 191757-
dc.identifier.wosid000584850000001-
dc.citation.endPage191757-
dc.citation.startPage191751-
dc.citation.titleIEEE ACCESS-
dc.citation.volume8-
dc.contributor.affiliatedAuthorYun-
dc.contributor.affiliatedAuthorJeung-
dc.contributor.affiliatedAuthorSong-
dc.contributor.affiliatedAuthorCHUNG, YOONYOUNG-
dc.identifier.scopusid2-s2.0-85102847705-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.type.docTypeArticle-
dc.subject.keywordPlusFREQUENCY-
dc.subject.keywordPlusEXERCISE-
dc.subject.keywordPlusPARAMETERS-
dc.subject.keywordPlusAMPLITUDE-
dc.subject.keywordPlusFEATURES-
dc.subject.keywordAuthorMuscles-
dc.subject.keywordAuthorFatigue-
dc.subject.keywordAuthorEstimation-
dc.subject.keywordAuthorLegged locomotion-
dc.subject.keywordAuthorCorrelation-
dc.subject.keywordAuthorSports-
dc.subject.keywordAuthorSpectral analysis-
dc.subject.keywordAuthorMuscle fatigue estimation-
dc.subject.keywordAuthorsurface electromyography-
dc.subject.keywordAuthormuscle mass-
dc.subject.keywordAuthorspectrum analysis-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTelecommunications-

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정윤영CHUNG, YOONYOUNG
Dept of Electrical Enginrg
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