• Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations. Part II: Objective comparison of vehicles

      Farah, G.; Petit-Boulanger, C.; Hewson, David; Duchêne, Jacques; Université de Technologie de Troyes; Technocentre Renault (Elsevier, 2006-02-02)
      The purpose of this study was to objectively assess the response of car passengers to lateral accelerations. Surface EMG signals were collected bilaterally from the cervical erector spinae (CES), latissimus dorsi (LD), erector spinae (ES), external oblique (EO), and vastus lateralis (VL) muscles of 10 subjects. Lateral acceleration was also recorded. Three chassis-seat configurations AA, BA and BB were tested, with the first letter denoting the chassis and the second the seat. SEMG signals were often contaminated by noise, and were, therefore, denoised using the methods explained in part I. Reciprocal phasic activity was observed for all muscles except for the EO, and the reaction of passengers to lateral accelerations was interpreted as a bust torsion. The RMS of EMG segments was used as an indication of muscle activity. Muscle activation of VL and ES were significantly affected by the configuration tested (p < 0.05), with greater activation levels observed for the chassis A than for the chassis B. Such a finding implies that greater roll requires greater muscle activity, thus resulting in less comfortable vehicles. Therefore, SEMG can be used to provide an objective measure of discomfort in passengers subjected to lateral accelerations in a car seat.
    • 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

      Farah, G.; Hewson, David; Duchêne, Jacques; Université de Technologie de Troyes; Technocentre Renault (Elsevier, 2006-02-02)
      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.