Noninvasive Monitoring of Dehydration of Fasting and Sportspeople Subjects via Skin Capacitance

• Published in: IEEE sensors journal Vol. 25; no. 7; pp. 11428 - 11440
• Main Authors: Siyoucef, Soumia, Adnane, Mourad, Rahman, Muhammad Mahboob Ur, Laleg-Kirati, Taous-Meriem, Al-Naffouri, Tareq Y.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Biomedical monitoring; Capacitance; Capacitive sensors; Dehydration; Dielectric properties; Electrocardiography; Fasting; Fluids; Hydration; Machine learning; machine learning (ML); Monitoring; Physics; Regression models; Sensors; Skin; skin capacitance; Smartphones; sportspeople; Touch screens; Touch sensitive screens
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2025.3538758

There is a critical need to design noninvasive, user-friendly hydration assessment tools across diverse population groups. In this article, we capitalize on the capacitive nature of smartphone screens for in situ dehydration monitoring in two population groups, i.e., fasting people and sportspeople, using smartphones. We utilize the FDC2114 board with its two capacitive sensors to emulate a smartphone touchscreen and demonstrate that the capacitive sensing could reliably differentiate between five distinct hydration levels of a fasting person and do dehydration detection for sportspeople. Our methodology involves collecting data from 35 fasting subjects five times a day between morning and evening at regular intervals, and from ten sportspeople before and after sports/exercise activity. This is followed by preprocessing, and 5-class, 4-class, and binary classification using a number of machine learning (ML) models. The results show that the linear logistic regression (LLR) model outperforms all other models for each population group, which demonstrates the sensitivity of capacitive sensing to the body's dielectric properties that vary with hydration levels. The study also reveals distinct dehydration patterns during fasting and exercising, which is mainly due to the sweat phenomenon that is more prominent in the sportspeople group. Therefore, we are of the opinion to train and fine-tune ML models for each population group separately.