Prediction of human core body temperature using non-invasive measurement methods

Area of Science:

• Physiology
• Occupational Health
• Biomedical Engineering

Background:

• Core body temperature monitoring is crucial for assessing heat stress in workers.
• Invasive methods for measuring core body temperature are impractical in occupational settings.
• Development of non-invasive techniques is essential for practical heat stress management.

Purpose of the Study:

• To define non-invasive measures for predicting core body temperature under diverse conditions.
• To evaluate the efficacy of selected physiological parameters in estimating core body temperature.
• To establish a reliable algorithm for real-time core body temperature assessment.

Main Methods:

• Two human subject studies were conducted with varying environmental temperatures (10°C, 30°C) and protocols.
• Non-invasive measurements included skin temperature, skin heat flux, and heart rate.
• Principle component analysis and multiple regression analysis were used to develop predictive models.

Main Results:

• Six parameters (three skin temperatures, two skin heat fluxes, heart rate) were identified.
• Two factors derived from these parameters effectively predicted core body temperature.
• The model achieved a root mean square deviation (rmsd) of 0.28°C to 0.34°C.

Conclusions:

• Multiple non-invasive physiological parameters are necessary for accurate core body temperature prediction.
• The developed algorithm provides a potential solution for real-time core body temperature monitoring.
• This approach can enhance health risk assessment for workers in various occupational environments.