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

Core body temperature (T-c) 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 (T-s), heat flux (HF), and heart rate (HR) to accurately estimate T-c 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 (similar to 350 and similar to 550 W) under three conditions (warm: 25 degrees C, 50% relative humidity (RH); hot-humid: 35 degrees C, 70% RH; and hot-dry: 40 degrees 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 T-c and T-s, 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 degrees C; bias 0.01 +/- 0.09 degrees C), rib (RMSE 0.18 0.09 C; bias 0.03 +/- 0.09 degrees C), and sternum (RMSE 0.20 +/- 0.10 degrees C; bias 0.04 +/- 0.13 degrees C) were found to have the lowest error values when using T-s, HF, and HR but, using only two of these measures provided similar accuracy.