Prediction of human core body temperature using non-invasive measurement methods
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The measurement of core body temperature is an efficient method for monitoring heat stress amongst workers in hot conditions. However, invasive measurement of core body temperature (e.g. rectal, intestinal, oesophageal temperature) is impractical for such applications. Therefore, the aim of this study was to define relevant non-invasive measures to predict core body temperature under various conditions. We conducted two human subject studies with different experimental protocols, different environmental temperatures (10 °C, 30 °C) and different subjects. In both studies the same non-invasive measurement methods (skin temperature, skin heat flux, heart rate) were applied. A principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. We identified six parameters (three skin temperatures, two skin heat fluxes and heart rate), which were included for the calculation of two factors. The predictive value of these factors for core body temperature was evaluated by a multiple regression analysis. The calculated root mean square deviation (rmsd) was in the range from 0.28 °C to 0.34 °C for all environmental conditions. These errors are similar to previous models using non-invasive measures to predict core body temperature. The results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. In addition, the physiological measurements chosen in this study and the algorithm defined in this work are potentially applicable as real-time core body temperature monitoring to assess health risk in broad range of working conditions.
KeywordsCore body temperature Skin temperature Heat flux Heat stress
The authors would like to thank the EU project consortium PROSPIE (FP7-NMP-229042), in particular George Havenith from Loughborough University for support with the experimental setup. Moreover, we thank all the subjects for participating in this study. In addition, we would like to thank Tero Mäkinen and Hannu Rintamäki from the Oulu Regional Institute of Occupational Health (Finland) for providing data from their study.
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