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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

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.
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https://hal-utt.archives-ouvertes.fr/hal-02362983
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Thursday, November 14, 2019 - 10:43:08 AM
Last modification on : Friday, November 15, 2019 - 1:36:54 AM

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Georges Farah, David Hewson, Jacques Duchene. 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 Electromyography and Kinesiology, Elsevier, 2006, 16 (6), pp.669-676. ⟨10.1016/j.jelekin.2005.11.010⟩. ⟨hal-02362983⟩

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