Machine learning-based non-invasive continuous dynamic monitoring of human core temperature with wearable dual temperature sensors

• Published in: Physiological measurement Vol. 46; no. 4; pp. 45002 - 45014
• Main Authors: Liang, Haotian, Wang, Yishan, Jiang, Linbo, Yu, Xinming, Xiong, Linghao, Luo, Liang, Fu, Le, Zhang, Yu, Li, Ye, Song, Jinzhong, Sun, Fangmin
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 30.04.2025
• Subjects: Adult; Body Temperature; core temperature monitoring; double temperature sensors; Female; Humans; Machine Learning; Male; Monitoring, Physiologic - instrumentation; non-invasive monitoring; wearable device; Wearable Electronic Devices; Young Adult; wearable device; core temperature monitoring; non-invasive monitoring; machine learning; double temperature sensors
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/1361-6579/adbf64

Objective. Due to the growing demand for personal health monitoring in extreme environments, continuous monitoring of core temperature has become increasingly important. Traditional monitoring methods, such as mercury thermometers and infrared thermometers, may have limitations in tracking real-time fluctuations in core temperature, especially in special application scenarios such as firefighting, military, and aerospace. This study aims to develop a non-invasive, continuous core temperature prediction model based on machine learning, addressing the limitations of traditional methods in extreme environments. Approach. This study develops a novel machine learning-based non-invasive continuous body core temperature monitoring model. A wearable dual temperature sensing device is designed to collect skin and environment temperature, six machine learning algorithms are trained utilizing data from 62 subjects. Main results. Performance evaluations on a test set of 10 subjects reveal outstanding results, achieving a mean absolute error of 0.15 °C ± 0.04 °C, a root mean square error of 0.17 °C ± 0.05 °C, and a mean absolute percentage error of 0.40% ± 0.12%. Statistical analysis further confirms the model’s superior predictive capability compared to traditional methods. Significance. The developed temperature monitoring model not only provides enhanced accuracy in various conditions but also serves as a robust tool for individual health monitoring. This innovation is particularly significant in scenarios requiring continuous and precise temperature tracking, and offering entirely new insights for improved health management strategies and outcomes.