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Wireless Tri-Axial Trunk Accelerometry Detects Deviations in Dynamic Center of Mass Motion Due to Running-Induced Fatigue

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  • Kurt H Schütte
  • Ellen A Maas
  • Vasileios Exadaktylos
  • Daniel Berckmans
  • Rachel E Venter
  • Benedicte Vanwanseele

Abstract

Small wireless trunk accelerometers have become a popular approach to unobtrusively quantify human locomotion and provide insights into both gait rehabilitation and sports performance. However, limited evidence exists as to which trunk accelerometry measures are suitable for the purpose of detecting movement compensations while running, and specifically in response to fatigue. The aim of this study was therefore to detect deviations in the dynamic center of mass (CoM) motion due to running-induced fatigue using tri-axial trunk accelerometry. Twenty runners aged 18–25 years completed an indoor treadmill running protocol to volitional exhaustion at speeds equivalent to their 3.2 km time trial performance. The following dependent measures were extracted from tri-axial trunk accelerations of 20 running steps before and after the treadmill fatigue protocol: the tri-axial ratio of acceleration root mean square (RMS) to the resultant vector RMS, step and stride regularity (autocorrelation procedure), and sample entropy. Running-induced fatigue increased mediolateral and anteroposterior ratios of acceleration RMS (p

Suggested Citation

  • Kurt H Schütte & Ellen A Maas & Vasileios Exadaktylos & Daniel Berckmans & Rachel E Venter & Benedicte Vanwanseele, 2015. "Wireless Tri-Axial Trunk Accelerometry Detects Deviations in Dynamic Center of Mass Motion Due to Running-Induced Fatigue," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-12, October.
    • Handle: RePEc:plo:pone00:0141957
    DOI: 10.1371/journal.pone.0141957
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    Cited by:

    1. Yu Tang Wen & Yu Ting Lee & Bing Shiang Yang & Yun Ju Lee, 2019. "Evaluation of Pollination by Honeybee (Apis Mellifera L.) on Canola (Brassica Napus L.) Produce," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 22(4), pages 16851-16859, November.
    2. Asif Razzaq & Khalida Hamid Abbasi & Muhammad Jamal & Asad Aslam & Kamil Malik & Muhammad Arshad Ullah, 2019. "Evaluation of Pollination by Honeybee (Apis Mellifera L.) on Canola (Brassica Napus L.) Produce," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 22(4), pages 16833-16836, November.
    3. Arne De Brabandere & Tim Op De Beéck & Kurt H Schütte & Wannes Meert & Benedicte Vanwanseele & Jesse Davis, 2018. "Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-17, June.

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