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Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review
Conor M Dolson1, Ethan R Harlow2,3, Dermot M Phelan4
1School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Sensors (Basel, Switzerland)
|October 14, 2022
Summary
Wearable technology can predict core body temperature (CBT) to prevent heat-related illnesses (HRI). Machine learning can improve accuracy by using individual and environmental data for personalized monitoring.
Area of Science:
- Sports Medicine
- Biomedical Engineering
- Environmental Health
Background:
- Heat-related illnesses (HRI) pose significant global health risks, with core body temperature (CBT) > 40°C indicating severe hyperthermia.
- Current CBT measurement methods are impractical for dynamic environments due to invasiveness, obstruction, or cost.
- Noninvasive CBT prediction via wearable technology offers potential for continuous monitoring and early HRI intervention.
Purpose of the Study:
- To systematically review the efficacy of wearable devices and predictive analytics in estimating CBT for HRI prevention.
- To identify gaps in current research regarding the integration of individual and environmental factors in CBT prediction algorithms.
Main Methods:
- A systematic review of 20 studies was conducted, analyzing 25 distinct algorithms for predicting CBT using wearable technology.
- Studies were evaluated for accuracy and clinical validity in predicting core body temperature.
Main Results:
- The review found high accuracy in CBT prediction, with 17 out of 18 algorithms meeting clinical validity standards.
- A significant limitation identified was the infrequent incorporation of individual and environmental data into prediction algorithms.
Conclusions:
- Wearable technology shows promise for accurate CBT prediction, but algorithms need enhancement.
- Robust machine learning incorporating diverse data is crucial for developing personalized and reliable CBT prediction algorithms.
- Integration with existing health systems can improve HRI prevention and clinical decision-making.
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