ArticleFrontiers in bioengineering and biotechnology2024
Predicting vertical ground reaction force characteristics during running with machine learning.
Article in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices.Sensors (Basel, Switzerland) · 2026Article
- Machine learning-based estimation of structure-specific load around the ankle and knee joint during running using IMU data.Frontiers in bioengineering and biotechnology · 2026Article
- Age- and step-length-dependent alterations in muscle synergies and joint coordination during unexpected gait termination.BMC geriatrics · 2025Article
- Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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