Effect of individual characteristics on a mathematical model of human thermoregulation

Abstract

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Predicting urban outdoor thermal comfort by the Universal Thermal Climate Index UTCI—a case study in Southern Brazil

“…This suggests that UTCI allows for adequate comparisons of climatic conditions and again confirms the simplified approach taken by UTCI to develop an assessment scale based on responses to reference conditions. Interestingly, the inclusion of individually recorded clothing insulation as additional input to the UTCI-Fiala model even impaired the predictive capacity, although individualised models performed better in other contexts (van Marken Lichtenbelt et al 2004;Havenith and Middendorp 1990). One reason might be that differences in the choice of clothing insulation under identical climate conditions, which are evident by the large interindividual variation observed in this study sample, could partly reflect differences in individual thermoregulatory systems.…”

An individualized human thermoregulation model for Chinese adults

“…Local autonomic regulation was employed to modify local sweat rates, local blood flows, and tissue metabolic rates [21]. Former studies indicated that major physiological difference between ethnicity and individuals lies in the passive system [11,22]. A standard Chinese model was established based on the Fiala model with modifications in body size and composition.…”

“…Body fat was calculated through the measurement of skinfold caliper, which needs the measurement of circumference at levels of umbilicus, minimal abdominal width at approximately midway between xiphoid and umbilicus. Metabolic rate (MR) and body composition (BC) were selected as input parameters for the Fiala model individualization [11]. MR was calculated from the O 2 consumption and CO 2 production measured by indirect calorimetry, using a ventilated hood system, and the model achieved the best accuracy with the input of MR and BC.…”

“…Their simulated result only showed that the normal body fat group had lower skin temperature but higher rectal temperature than the lean group, yet a future work including more validations with experiments needs to be done. Study carried out by van Marken Lichtenbelt et al [11] shows that on a group level predictions of skin temperatures can be improved when adopting individualized body characteristics and measured metabolic rate, but the predictions on an individual level were not improved. Fiala model [12] was individualized by Lichtenbelt et al with respect to anthropometrics, body fat, and metabolic rate using the physiological data of Dutchman [13]; predictions of the individualized model were improved but a significant error remained.…”

Thermal comfort modelling of body temperature and psychological variations of a human exercising in an outdoor environment

“…The current study employed a multi-segment method using four segments (Table 2) with respect to location of skin thermocouples and accounting for covered or bare body parts. This new method is in accordance with thermal energy budget models by Stolwijk and Hardy (1966), Fiala et al (2001), Huizenga et al (2001), van Marken Lichtenbelt et al (2004), and Munir et al (2009). Each body segment was assumed to possess a distinct T sk , r c , and aerodynamic resistance (r a ) to estimate convective heat exchange (C).…”