Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920446 | Computers in Biology and Medicine | 2018 | 21 Pages |
Abstract
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 22â¯Â±â¯4â¯yr, height 1.75â¯Â±â¯0.10â¯m, body mass 76.4â¯Â±â¯10.7â¯kg, and body fat 23.4â¯Â±â¯5.8%, meanâ¯Â±â¯standard deviation) while walking at two different metabolic rates (â¼350 and â¼550â¯W) under three conditions (warm: 25â¯Â°C, 50% relative humidity (RH); hot-humid: 35â¯Â°C, 70% RH; and hot-dry: 40â¯Â°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 0.18â¯Â±â¯0.04â¯Â°C; bias â0.01â¯Â±â¯0.09â¯Â°C), rib (RMSE 0.18â¯Â±â¯0.09â¯Â°C; bias â0.03â¯Â±â¯0.09â¯Â°C), and sternum (RMSE 0.20â¯Â±â¯0.10â¯Â°C; bias â0.04â¯Â±â¯0.13â¯Â°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.
Related Topics
Physical Sciences and Engineering
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Computer Science Applications
Authors
Alexander P. Welles, Xiaojiang Xu, William R. Santee, David P. Looney, Mark J. Buller, Adam W. Potter, Reed W. Hoyt,