首页|Research Findings from University of Leuven (KU Leuven) UpdateUnderstanding of Machine Learning (Predicting vertical ground reactionforce characteristics duri ng running with machine learning)
Research Findings from University of Leuven (KU Leuven) UpdateUnderstanding of Machine Learning (Predicting vertical ground reactionforce characteristics duri ng running with machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New study results on artificial intell igence have been published. According to newsreporting from Leuven, Belgium, by NewsRx journalists, research stated, “Running poses a high risk ofdeveloping r unning-related injuries (RRIs).”Financial supporters for this research include Fonds Wetenschappelijk Onderzoek; Onderzoeksraad, KuLeuven.Our news editors obtained a quote from the research from University of Leuven (K U Leuven): “Themajority of RRIs are the result of an imbalance between cumulati ve musculoskeletal load and load capacity.A general estimate of whole-body biom echanical load can be inferred from ground reaction forces (GRFs).Unfortunately , GRFs typically can only be measured in a controlled environment, which hinders its widerapplicability. The advent of portable sensors has enabled training ma chine-learned models that are able tomonitor GRF characteristics associated wit h RRIs in a broader range of contexts. Our study presents andevaluates a machin e-learning method to predict the contact time, active peak, impact peak, and imp ulseof the vertical GRF during running from three-dimensional sacral accelerati on. The developed models forpredicting active peak, impact peak, impulse, and c ontact time demonstrated a root-mean-squared errorof 0.080 body weight (BW), 0. 198 BW, 0.0073 BW seconds, and 0.0101 seconds, respectively.”
University of Leuven (KU Leuven)LeuvenBelgiumEuropeCyborgsEmerging TechnologiesMachine LearningRisk and Pr evention
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