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Assessment of Whole Body and Local Muscle Fatigue Using Electromyography and a Perceived Exertion Scale for Squat Lifting

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  • Imran Ahmad

    (Department of Industrial Management Engineering, Hanyang University, Ansan 15588, Korea)

  • Jung-Yong Kim

    (Department of Industrial Management Engineering, Hanyang University, Ansan 15588, Korea)

Abstract

This research study aims at addressing the paradigm of whole body fatigue and local muscle fatigue detection for squat lifting. For this purpose, a comparison was made between perceived exertion with the heart rate and normalized mean power frequency (NMPF) of eight major muscles. The sample consisted of 25 healthy males (age: 30 ± 2.2 years). Borg’s CR-10 scale was used for perceived exertion for two segments of the body (lower and upper) and the whole body. The lower extremity of the body was observed to be dominant compared to the upper and whole body in perceived response. First mode of principal component analysis (PCA) was obtained through the covariance matrix for the eight muscles for 25 subjects for NMPF of eight muscles. The diagonal entries in the covariance matrix were observed for each muscle. The muscle with the highest absolute magnitude was observed across all the 25 subjects. The medial deltoid and the rectus femoris muscles were observed to have the highest frequency for each PCA across 25 subjects. The rectus femoris, having the highest counts in all subjects, validated that the lower extremity dominates the sense of whole body fatigue during squat lifting. The findings revealed that it is significant to take into account the relation between perceived and measured effort that can help prevent musculoskeletal disorders in repetitive occupational tasks.

Suggested Citation

  • Imran Ahmad & Jung-Yong Kim, 2018. "Assessment of Whole Body and Local Muscle Fatigue Using Electromyography and a Perceived Exertion Scale for Squat Lifting," IJERPH, MDPI, vol. 15(4), pages 1-12, April.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:4:p:784-:d:141706
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    References listed on IDEAS

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    1. I.S. Dhindsa & R. Agarwal & H.S. Ryait, 2017. "Principal component analysis-based muscle identification for myoelectric-controlled exoskeleton knee," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1707-1720, July.
    2. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    3. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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