Predicting vertical ground reaction force characteristics during running with machine learning.
| Autor: | Bogaert S; Human Movements Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium., Davis J; Department of Computer Science, Leuven.AI, KU Leuven, Leuven, Belgium., Vanwanseele B; Human Movements Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in bioengineering and biotechnology [Front Bioeng Biotechnol] 2024 Oct 08; Vol. 12, pp. 1440033. Date of Electronic Publication: 2024 Oct 08 (Print Publication: 2024). |
| DOI: | 10.3389/fbioe.2024.1440033 |
| Abstrakt: | Running poses a high risk of developing running-related injuries (RRIs). The majority of RRIs are the result of an imbalance between cumulative musculoskeletal load and load capacity. A general estimate of whole-body biomechanical load can be inferred from ground reaction forces (GRFs). Unfortunately, GRFs typically can only be measured in a controlled environment, which hinders its wider applicability. The advent of portable sensors has enabled training machine-learned models that are able to monitor GRF characteristics associated with RRIs in a broader range of contexts. Our study presents and evaluates a machine-learning method to predict the contact time, active peak, impact peak, and impulse of the vertical GRF during running from three-dimensional sacral acceleration. The developed models for predicting active peak, impact peak, impulse, and contact time demonstrated a root-mean-squared error of 0.080 body weight (BW), 0.198 BW, 0.0073 BW ⋅ seconds, and 0.0101 seconds, respectively. Our proposed method outperformed a mean-prediction baseline and two established methods from the literature. The results indicate the potential utility of this approach as a valuable tool for monitoring selected factors related to running-related injuries. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Bogaert, Davis and Vanwanseele.) |
| Databáze: | MEDLINE |
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