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Article: Time-dependent power spectral density estimation of surface electromyography during isometric muscle contraction: Methods and comparisons

TitleTime-dependent power spectral density estimation of surface electromyography during isometric muscle contraction: Methods and comparisons
Authors
KeywordsFatigue analysis
Isometric muscle contraction
PSD estimation
Surface EMG
Time-frequency analysis
Issue Date2010
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/jelekin
Citation
Journal Of Electromyography And Kinesiology, 2010, v. 20 n. 1, p. 89-101 How to Cite?
AbstractThis paper studies the time-dependent power spectral density (PSD) estimation of nonstationary surface electromyography (SEMG) signals and its application to fatigue analysis during isometric muscle contraction. The conventional time-dependent PSD estimation methods exhibit large variabilities in estimating the instantaneous SEMG parameters so that they often fail to identify the changing patterns of short-period SEMG signals and gauge the extent of fatigue in specific muscle groups. To address this problem, a time-varying autoregressive (TVAR) model is proposed in this paper to describe the SEMG signal, and then the recursive least-squares (RLS) and basis function expansion (BFE) methods are used to estimate the model coefficients and the time-dependent PSD. The instantaneous parameters extracted from the PSD estimation are evaluated and compared in terms of reliability, accuracy, and complexity. Experimental results on synthesized and real SEMG data show that the proposed TVAR-model-based PSD estimators can achieve more stable and precise instantaneous parameter estimation than conventional methods. © 2008.
Persistent Identifierhttp://hdl.handle.net/10722/58749
ISSN
2015 Impact Factor: 1.53
2015 SCImago Journal Rankings: 0.886
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of the Hong Kong SAR, ChinaGRP 712408E
S.K. Yee Medical Foundation207210/203210
Funding Information:

This work was partially supported by grants from the Research Grants Council of the Hong Kong SAR, China (GRP 712408E) and S.K. Yee Medical Foundation (207210/203210).

References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorLiu, HTen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorLuk, KDKen_HK
dc.contributor.authorHu, Yen_HK
dc.date.accessioned2010-05-31T03:36:14Z-
dc.date.available2010-05-31T03:36:14Z-
dc.date.issued2010en_HK
dc.identifier.citationJournal Of Electromyography And Kinesiology, 2010, v. 20 n. 1, p. 89-101en_HK
dc.identifier.issn1050-6411en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58749-
dc.description.abstractThis paper studies the time-dependent power spectral density (PSD) estimation of nonstationary surface electromyography (SEMG) signals and its application to fatigue analysis during isometric muscle contraction. The conventional time-dependent PSD estimation methods exhibit large variabilities in estimating the instantaneous SEMG parameters so that they often fail to identify the changing patterns of short-period SEMG signals and gauge the extent of fatigue in specific muscle groups. To address this problem, a time-varying autoregressive (TVAR) model is proposed in this paper to describe the SEMG signal, and then the recursive least-squares (RLS) and basis function expansion (BFE) methods are used to estimate the model coefficients and the time-dependent PSD. The instantaneous parameters extracted from the PSD estimation are evaluated and compared in terms of reliability, accuracy, and complexity. Experimental results on synthesized and real SEMG data show that the proposed TVAR-model-based PSD estimators can achieve more stable and precise instantaneous parameter estimation than conventional methods. © 2008.en_HK
dc.languageengen_HK
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/jelekinen_HK
dc.relation.ispartofJournal of Electromyography and Kinesiologyen_HK
dc.subjectFatigue analysisen_HK
dc.subjectIsometric muscle contractionen_HK
dc.subjectPSD estimationen_HK
dc.subjectSurface EMGen_HK
dc.subjectTime-frequency analysisen_HK
dc.subject.meshAlgorithms-
dc.subject.meshBack - physiology-
dc.subject.meshElectromyography - methods-
dc.subject.meshIsometric Contraction - physiology-
dc.subject.meshMuscle, Skeletal - physiology-
dc.titleTime-dependent power spectral density estimation of surface electromyography during isometric muscle contraction: Methods and comparisonsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1050-6411&volume=20&issue=1&spage=89&epage=101&date=2010&atitle=Time-dependent+power+spectral+density+estimation+of+surface+electromyography+during+isometric+muscle+contraction:+methods+and+comparisons-
dc.identifier.emailZhang, ZG:zgzhang@eee.hku.hken_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailLuk, KDK:hcm21000@hku.hken_HK
dc.identifier.emailHu, Y:yhud@hku.hken_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityLuk, KDK=rp00333en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jelekin.2008.09.007en_HK
dc.identifier.pmid19027325-
dc.identifier.scopuseid_2-s2.0-71349088619en_HK
dc.identifier.hkuros180200en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-71349088619&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume20en_HK
dc.identifier.issue1en_HK
dc.identifier.spage89en_HK
dc.identifier.epage101en_HK
dc.identifier.isiWOS:000273621100013-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridLiu, HT=26643490700en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridLuk, KDK=7201921573en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK

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