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Can we predict the internal load (perceived exertion) from the external load (GPS) with machine learning?

Abstract : Machine learning methods have already highlighted the potential relationships between external and internal load indicators in soccer 1,2. In addition, several studies have investigated the relationship between internal and external load data in different team sports using linear analyses, among others 3,4,5,6. All analysis were done at the group level and for each player. The first objective of this work was to study the correlation between subjective internal load data collected with the rating of perceived exertion scale in session (s-RPE) and objective external load assessed with GPS and integrated inertial sensors. The second objective was to explain internal load from the external load dataset with a particular focus on the acceleration-related PlayerLoad™variable, a hypothetical indicator of mechanical load 7.
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Submitted on : Tuesday, November 23, 2021 - 2:51:09 PM
Last modification on : Friday, August 5, 2022 - 10:58:24 AM


  • HAL Id : hal-03443310, version 1


Emmanuel Vallance, Nicolas Sutton-Charani, Patrice Guyot, S. Perrey. Can we predict the internal load (perceived exertion) from the external load (GPS) with machine learning?. ACAPS 2021 - 19ème congrès international des chercheurs en Activités Physiques et Sportives, Oct 2021, Montpellier, France. ⟨hal-03443310⟩



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