Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 9; pp. 4725 - 4732
• Main Authors: Wang, Shu, Lafaye, Celine, Saubade, Mathieu, Besson, Cyril, Margarit-Taule, Josep Maria, Gremeaux, Vincent, Liu, Shih-Chii
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Biological system modeling; Biomarkers; Biomedical monitoring; Body weight; Body weight loss; Dehydration; Endurance; exercise; Fluid intake; Heart rate; Hydration; Learning algorithms; Machine learning; Physical training; physiological biomarkers; Physiology; Predictive models; Sodium; Sweat; sweat biomarkers; Temperature measurement; Weight loss

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 f...