We evaluated a Kalman filter (KF) approach to modeling the physiology of internal temperature viewed through "noisy" non-invasive observations of heart rate. Human core body temperature (Tcore) is an important measure of thermal state, e.g., hypo- or hyperthermia, but is difficult to measure using non-invasive wearable sensors. We estimated parameters for a discrete KF model from data collected during several Military training events and from distance runners (n=38). Model performance was evaluated in 25 physically-active subjects who participated in various laboratory and field studies involving exercise of 2-to-8 h duration at ambient temperatures of 20 to 40°C. Overall, the KF model's estimate of Tcore had a root mean square error of 0.30±0.13 °C from the observed Tcore, and was within ± 0.5 °C over 85% of the time. The benefit of the KF approach is that it requires only one input while current state of the art models typically require multiple inputs including individual anthropometries, metabolic rate, clothing characteristics, and environmental conditions. This state estimation problem in computational physiology illustrates the potential for collaboration between the artificial intelligence and ambulatory physiological monitoring communities.
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