Estimation of core body temperature by near-infrared imaging of vein diameter change in the dorsal hand

Abstract

Core body temperature (Tcore) is a key indicator of personal thermal comfort and serves as a monitor of thermal strain. Multi-parametric sensors are not practical for estimating core temperature because they require long data collection times and a wide variety of settings. This study introduces dorsal hand vein dynamics as a novel indicator along with heart rate (HR) and dorsal hand skin temperature (Thand) for predicting Tcore during rest following Tcore elevation. Twelve healthy males aged 27 ± 9 years old participated in the experiment. The experimental procedure consisted of a 10-min rest followed by 60 min of passive heat stress induced by leg immersion in hot water at 42°C and a 40-min thermal relaxation period after the legs were removed from the water. A near-infrared (NIR) imaging system was configured to monitor the dorsal hand veins during the entire experimental session. The values of HR, Thand, and Tcore were continuously monitored while the ambient temperature and relative humidity (RH) were maintained in a climate chamber at 20°C and 50%, respectively. Our selected predictor parameters demonstrated similar patterns in the Tcore such that the value increased as a result of passive heat stress and decreased in the thermal relaxation phase. The experimental data were divided into two phases: thermal stress and relaxation. At the resting condition, inclusion of the hand vein diameter (VD) improved the multiple linear regression value (R2) about 26%. At the relaxation phase, however, training regressions R2 = 0.68 and R2 = 0.94 were observed in the regression model with and without considering VD, respectively. The test regression value of R2 = 0.88 and the root mean square error (RMSE) of 0.18°C showed good agreement with the predicted values. These findings demonstrate acceptable validity of the non-invasive Tcore estimation at the resting condition. In particular, the inclusion of VD as a predictor in the regression analysis increases the prediction accuracy with a lower RMSE value.