Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study-Reference-Cited by-同舟云学术

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

Published:2022-09 Issue:9 Volume:26 Page:4725-4732
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Wang Shu¹^ORCID,Lafaye Celine²,Saubade Mathieu²,Besson Cyril²^ORCID,Margarit-Taule Josep Maria³^ORCID,Gremeaux Vincent²,Liu Shih-Chii¹^ORCID

Affiliation:

1. Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland

2. Sports Medicine Unit, Swiss Olympic Medical Center, Division of Physical Medicine and Rehabilitation, Lausanne University Hospital, Lausanne, Switzerland

3. Instituto de Microelectrónica de Barcelona (IMB-CNM), CSIC, Barcelona, Spain

Funder

SNSF-Sinergia WeCare

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9882959/09806186.pdf?arnumber=9806186

Reference41 articles.

1. Considerations in the Use of Body Mass Change to Estimate Change in Hydration Status During a 161-Kilometer Ultramarathon Running Competition

2. Pharmacokinetic analysis of absorption, distribution and disappearance of ingested water labeled with D2O in humans

3. Support-vector networks

4. A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts

5. Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

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