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Article: Estimation of muscle fatigue degree using time-varying autoregressive model parameter estimation of surface electromyography

TitleEstimation of muscle fatigue degree using time-varying autoregressive model parameter estimation of surface electromyography
Authors
KeywordsMuscle Fatigue
Surface Electromyography
Time - Varying Autoregressive Model
Issue Date2007
Citation
Chinese Journal Of Biomedical Engineering, 2007, v. 26 n. 4, p. 493-497 How to Cite?
AbstractObjective: Aiming at investigating the nonstationary characters of the EMG signal, time-varying AR model was employed in this study to quickly estimate muscle fatigue by analyzing short time surface electromyography. Methods: Data from 10 subjects pre- and post-fatigue was analyzed by the AR model. A recursive least squares algorithm was then used to extract the time-varying parameters and transformed the time-varying question into time-stable one. Results: The first time-varying parameter shows higher sensitivity to fatigue than that of the traditional median frequency (sensitivities increased from 37.80% to 324.46%). Conclusions: The mean value of the first time-varying parameter could be used as a fast indicator to reflect the fatigue of the muscle, which would promote practical applications in the field of lumbar muscle fatigue diagnosis and rehabilitation. Also it would provide a reliable tool for the study of ergonomics.
Persistent Identifierhttp://hdl.handle.net/10722/170114
ISSN
2015 SCImago Journal Rankings: 0.109
References

 

DC FieldValueLanguage
dc.contributor.authorLiu, HTen_US
dc.contributor.authorCao, YZen_US
dc.contributor.authorXie, XBen_US
dc.contributor.authorHu, Yen_US
dc.date.accessioned2012-10-30T06:05:24Z-
dc.date.available2012-10-30T06:05:24Z-
dc.date.issued2007en_US
dc.identifier.citationChinese Journal Of Biomedical Engineering, 2007, v. 26 n. 4, p. 493-497en_US
dc.identifier.issn0258-8021en_US
dc.identifier.urihttp://hdl.handle.net/10722/170114-
dc.description.abstractObjective: Aiming at investigating the nonstationary characters of the EMG signal, time-varying AR model was employed in this study to quickly estimate muscle fatigue by analyzing short time surface electromyography. Methods: Data from 10 subjects pre- and post-fatigue was analyzed by the AR model. A recursive least squares algorithm was then used to extract the time-varying parameters and transformed the time-varying question into time-stable one. Results: The first time-varying parameter shows higher sensitivity to fatigue than that of the traditional median frequency (sensitivities increased from 37.80% to 324.46%). Conclusions: The mean value of the first time-varying parameter could be used as a fast indicator to reflect the fatigue of the muscle, which would promote practical applications in the field of lumbar muscle fatigue diagnosis and rehabilitation. Also it would provide a reliable tool for the study of ergonomics.en_US
dc.languageengen_US
dc.relation.ispartofChinese Journal of Biomedical Engineeringen_US
dc.subjectMuscle Fatigueen_US
dc.subjectSurface Electromyographyen_US
dc.subjectTime - Varying Autoregressive Modelen_US
dc.titleEstimation of muscle fatigue degree using time-varying autoregressive model parameter estimation of surface electromyographyen_US
dc.typeArticleen_US
dc.identifier.emailHu, Y:yhud@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-34547917248en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547917248&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume26en_US
dc.identifier.issue4en_US
dc.identifier.spage493en_US
dc.identifier.epage497en_US
dc.identifier.scopusauthoridLiu, HT=26643490700en_US
dc.identifier.scopusauthoridCao, YZ=13104339000en_US
dc.identifier.scopusauthoridXie, XB=53870912800en_US
dc.identifier.scopusauthoridHu, Y=7407116091en_US

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