Surface electromyography analysis in long-term recordings: application to head rest comfort in cars.
Analysis of long-term surface electromyographic (SEMG) signals has many applications in ergonomics when related to muscle fatigue. The present work proposes a set of processing methods reporting SEMG modifications during longterm driving tests in various situations (with or without head rest). A seg...
| Publicado en: | Ergonomics Vol. 44; no. 3; pp. 313 - 328 |
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| Autores principales: | , |
| Formato: | clinical trial research Journal Article |
| Publicado: |
Taylor & Francis Ltd
2/20/2001
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=106092513&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106092513 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: 2/20/2001 vid: 44 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 106092513 106092513 2009432745 10.1080/00140130118427 NLM11219762 106092513 ppf: 313 ppct: 15 formats: tig: atl: Surface electromyography analysis in long-term recordings: application to head rest comfort in cars. aug: au: Duchêne J Lamotte T sug: subj: Automobile Driving Electromyography Methods Ergonomics Muscle Fatigue Physiology Posture Physiology Algorithms Clinical Trials Female Male Models, Theoretical Time Factors Human Female Male ab: Analysis of long-term surface electromyographic (SEMG) signals has many applications in ergonomics when related to muscle fatigue. The present work proposes a set of processing methods reporting SEMG modifications during longterm driving tests in various situations (with or without head rest). A segmentation/classification algorithm allows signal splitting into homogeneous parts (postural activity and EMG bursts) and an efficient artefact suppression. Postural activity modifications are evaluated from time-varying amplitude probability density function (TAPDF) parameters. EMG burst analysis is achieved taking into account the relationships of these bursts with accelerometric events. This segmentation/classification procedure improves repeatability but does not significantly modify the overall results obtained before segmentation, as far as the analysis of head rest influence is concerned. pubtype: Academic Journal doctype: clinical trial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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