Human thermoregulatory system state estimation using non-invasive physiological sensors.

Small teams of emergency workers/military can often find themselves engaged in critical, high exertion work conducted under challenging environmental conditions. These types of conditions present thermal work strain challenges which unmitigated can lead to collapse (heat exhaustion) or even death from heat stroke. Physiological measurement of these teams provides a mechanism that could be an effective tool in preventing thermal injury. While indices of thermal work strain have been proposed they suffer from ignoring thermoregulatory context and rely on measuring internal temperature (IT). Measurement of IT in free ranging ambulatory environments is problematic. In this paper we propose a physiology based Dynamic Bayesian Network (DBN) model that estimates internal temperature, heat production and heat transfer from observations of heart rate, accelerometry, and skin heat flux. We learn the model's conditional probability distributions from seven volunteers engaged in a 48 hour military field training exercise. We demonstrate that sum of our minute to minute heat production estimates correlate well with total daily energy expenditure (TDEE) measured using the doubly labeled water technique (r(2) = 0.73). We also demonstrate that the DBN is able to infer IT in new datasets to within ±0.5 °C over 85% of the time. Importantly, the additional thermoregulatory context allows critical high IT temperature to be estimated better than previous approaches. We conclude that the DBN approach shows promise in enabling practical real time thermal work strain monitoring applications from physiological monitoring systems that exist today.