Publication: Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study
Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study
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Wang, S., Lafaye, C., Saubade, M., Besson, C., Margarit-Taule, J. M., Gremeaux, V., & Liu, S.-C. (2022). Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study. IEEE Journal of Biomedical and Health Informatics, 26(9), 4725–4732. https://doi.org/10.1109/jbhi.2022.3186150
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Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured
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- Wang, Shu
- Lafaye, Celine
- Saubade, Mathieu
- Besson, Cyril
- Margarit-Taule, Josep Maria
- Gremeaux, Vincent
- Liu, Shih-Chii
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Wang, S., Lafaye, C., Saubade, M., Besson, C., Margarit-Taule, J. M., Gremeaux, V., & Liu, S.-C. (2022). Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study. IEEE Journal of Biomedical and Health Informatics, 26(9), 4725–4732. https://doi.org/10.1109/jbhi.2022.3186150
