4.7 Article

Principal Component Analysis of the Running Ground Reaction Forces With Different Speeds

Journal

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2021.629809

Keywords

gait biomechanics; gender difference; machine learning; running velocity; ground reaction force

Funding

  1. Badminton World Federation
  2. K.C. Wong Magna Fund in Ningbo University
  3. Loudi Technical College

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This study investigated the variances of GRFs in rearfoot striking runners across incremental speeds, finding differences between male and female runners in braking and propulsive forces, as well as timing of propulsion. The results may have implications for preventing sex-specific running-related injuries.
Ground reaction force (GRF) is a key metric in biomechanical research, including parameters of loading rate (LR), first impact peak, second impact peak, and transient between first and second impact peaks in heel strike runners. The GRFs vary over time during stance. This study was aimed to investigate the variances of GRFs in rearfoot striking runners across incremental speeds. Thirty female and male runners joined the running tests on the instrumented treadmill with speeds of 2.7, 3.0, 3.3, and 3.7 m/s. The discrete parameters of vertical average loading rate in the current study are consistent with the literature findings. The principal component analysis was modeled to investigate the main variances (95%) in the GRFs over stance. The females varied in the magnitude of braking and propulsive forces (PC1, 84.93%), whereas the male runners varied in the timing of propulsion (PC1, 53.38%). The female runners dominantly varied in the transient between the first and second peaks of vertical GRF (PC1, 36.52%) and LR (PC2, 33.76%), whereas the males variated in the LR and second peak of vertical GRF (PC1, 78.69%). Knowledge reported in the current study suggested the difference of the magnitude and patterns of GRF between male and female runners across different speeds. These findings may have implications for the prevention of sex-specific running-related injuries and could be integrated with wearable signals for the in-field prediction and estimation of impact loadings and GRFs.

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