Estimation of core body temperature from skin temperature, heat flux, and heart rate using a Kalman filter.

Core body temperature (TC) is a key physiological metric of thermal heat-strain yet it remains difficult to measure non-invasively in the field. This work used combinations of observations of skin temperature (TS), heat flux (HF), and heart rate (HR) to accurately estimate TC using a Kalman Filter (KF). Data were collected from eight volunteers (age 22 ± 4 yr, height 1.75 ± 0.10 m, body mass 76.4 ± 10.7 kg, and body fat 23.4 ± 5.8%, mean ± standard deviation) while walking at two different metabolic rates (∼350 and ∼550 W) under three conditions (warm: 25 °C, 50% relative humidity (RH); hot-humid: 35 °C, 70% RH; and hot-dry: 40 °C, 20% RH). Skin temperature and HF data were collected from six locations: pectoralis, inner thigh, scapula, sternum, rib cage, and forehead. Kalman filter variables were learned via linear regression and covariance calculations between TC and TS, HF, and HR. Root mean square error (RMSE) and bias were calculated to identify the best performing models. The pectoralis (RMSE 0.18 ± 0.04 °C; bias -0.01 ± 0.09 °C), rib (RMSE 0.18 ± 0.09 °C; bias -0.03 ± 0.09 °C), and sternum (RMSE 0.20 ± 0.10 °C; bias -0.04 ± 0.13 °C) were found to have the lowest error values when using TS, HF, and HR but, using only two of these measures provided similar accuracy.