Individualized estimation of human core body temperature using noninvasive measurements

Area of Science:

• Physiology
• Biomedical Engineering
• Environmental Health

Background:

• Rising core body temperature (Tc) during strenuous activity indicates heat injury risk.
• Real-time Tc monitoring is crucial for preventing heat injuries but requires impractical invasive methods.
• Existing methods lack practicality for field-based, continuous core body temperature assessment.

Purpose of the Study:

• To develop a mathematical model for real-time, noninvasive estimation of core body temperature (Tc).
• To create a personalized system for early warning of heat injury risk.
• To validate the model's accuracy and robustness under various conditions.

Main Methods:

• Developed a mathematical model linking Tc to noninvasive physiological (heart rate, skin temperature) and environmental (ambient temperature, humidity) data.
• Employed a Kalman filter for on-the-fly adaptation of model parameters to individual subjects.
• Validated the model using data from 166 subjects across three studies involving treadmill and cycle ergometer tasks.

Main Results:

• Achieved an overall average root mean squared error (RMSE) of 0.33°C for individualized Tc estimates.
• Demonstrated a lower average RMSE of 0.25°C for subjects with Tc exceeding 38.5°C.
• Maintained model robustness with up to 40% missing data or added noise, showing no more than 16% increase in RMSE.

Conclusions:

• The individualized mathematical model provides a practical and effective noninvasive surrogate for core body temperature monitoring.
• The system enables real-time personalized heat-stress response learning and early warning of heat injury risk.
• This approach overcomes limitations of invasive technologies, offering a viable solution for heat injury prevention.