A Hybrid Core Temperature Estimation Method Integrating Physiological and Environmental Factors

Accurate estimation of core body temperature (Tc) is vital for monitoring thermoregulation and preventing heat-related illnesses under extreme environmental conditions. Invasive measurements are unsuitable for continuous use, motivating noninvasive computational alternatives. This study proposes a hybrid Tc estimation method that integrates physiological signals, including heart rate (HR) and skin temperature (Ts), with environmental temperature (Ta) and humidity. The approach combines two differential equation-based thermoregulation models with a random forest regression model to improve predictive performance. Model parameters were jointly optimized, and performance was validated using leave-one-out cross-validation. The hybrid method achieved an RMSE of 0.24ºC and a 95% limits of agreement of [−0.47ºC, 0.47ºC].