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    Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings

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    Authors
    Farah, G.
    Hewson, David
    Duchêne, Jacques
    Affiliation
    Université de Technologie de Troyes
    Technocentre Renault
    Issue Date
    2006-02-02
    Subjects
    surface electromyography
    noise reduction
    classification expectation-maximization (CEM)
    autoregressive modeling
    
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    Abstract
    The aim of this paper is to develop a method to extract relevant activities from surface electromyography (SEMG) recordings under difficult experimental conditions with a poor signal to noise ratio. High amplitude artifacts, the QRS complex, low frequency noise and white noise significantly alter EMG characteristics. The CEM algorithm proved to be useful for segmentation of SEMG signals into high amplitude artifacts (HAA), phasic activity (PA) and background postural activity (BA) classes. This segmentation was performed on signal energy, with classes belonging to a χ2 distribution. Ninety-five percent of HAA events and 96.25% of BA events were detected, and the remaining noise was then identified using AR modeling, a classification based upon the position of the coordinates of the pole of highest module. This method eliminated 91.5% of noise and misclassified only 3.3% of EMG events when applied to SEMG recorded on passengers subjected to lateral accelerations.
    Citation
    Farah G, Hewson DJ, Duchene J (2006) 'Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings', Journal of Electro - myography and Kinesiology, 16 (6), pp.669-676.
    Publisher
    Elsevier
    Journal
    Journal of Electromyography and Kinesiology
    URI
    http://hdl.handle.net/10547/623455
    DOI
    10.1016/j.jelekin.2005.11.010
    PubMed ID
    16458024
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S1050641105001458
    Type
    Article
    Language
    en
    ISSN
    1050-6411
    EISSN
    1873-5711
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jelekin.2005.11.010
    Scopus Count
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    Health

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