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BRIEF RESEARCH REPORT article

Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1440033
This article is part of the Research Topic Assessment and Monitoring of Human Movement View all 29 articles

Predicting Vertical Ground Reaction Force Characteristics during Running with Machine Learning

Provisionally accepted
Sieglinde Bogaert Sieglinde Bogaert 1*Jesse Davis Jesse Davis 2,3Benedicte Vanwanseele Benedicte Vanwanseele 1
  • 1 Human Movement Biomechanics Research Group, Department of Movements Sciences, KU Leuven, Leuven, Belgium
  • 2 Department of Computer Science, Faculty of Sciences, KU Leuven, Leuven, Belgium
  • 3 Institute for Artificial Intelligence, KU Leuven, Leuven, Belgium

The final, formatted version of the article will be published soon.

    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, 0.0075 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.

    Keywords: Running, machine learning, Vertical ground reaction force, Inertial measurement unit, Contact time, Active peak, Impact peak, impulse

    Received: 28 May 2024; Accepted: 20 Sep 2024.

    Copyright: © 2024 Bogaert, Davis and Vanwanseele. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sieglinde Bogaert, Human Movement Biomechanics Research Group, Department of Movements Sciences, KU Leuven, Leuven, Belgium

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.