Validation of the thermophysiological model by Fiala for prediction of local skin temperatures (original) (raw)
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Prediction of the average skin temperature in warm and hot environments
European Journal of Applied Physiology, 2000
The prediction of the mean skin temperature used for the Required Sweat Rate index was criticised for not being valid in conditions with high radiation and high humidity. Based on a large database provided by 9 institutes, 1999 data points obtained using steady-state conditions, from 1399 experiments and involving 377 male subjects, were used for the development of a new prediction model. The observed mean skin temperatures ranged from 30.7 °C to 38.6 °C. Experimental conditions included air temperatures (T a) between 20 and 55 °C, mean radiant temperatures (T r) up to 145 °C, partial vapour pressures (P a) from 0.2 to 5.3 kPa, air velocities (v a) between 0.1 and 2 m/s, and metabolic rates (M) from 102 to 620 W. Rectal temperature (T re) was included in the models to increase the accuracy of prediction. Separate models were derived for nude (clothing insulation, Icl, ≤0.2 clo, where 1 clo=0.155 m2 · °C · W−1, which is equivalent to the thermal insulation of clothing necessary to maintain a resting subject in comfort in a normally ventilated room, air movement=10 cm/s, at a temperature of 21 °C and a humidity of less than 50%) and clothed (0.6 ≤ Icl ≤ 1.0 clo) subjects using a multiple linear regression technique with re-sampling (non-parametric bootstrap). The following expressions were obtained for nude and clothed subjects, respectively: T sk=7.19 + 0.064T a + 0.061T r + 0.198P a− 0.348v a + 0.616T re and T sk=12.17 + 0.020T a + 0.044T r + 0.194P a − 0.253v a + 0.0029M + 0.513T re. For the nude and clothed subjects, 83.3% and 81.8%, respectively, of the predicted skin temperatures were within the range of ±1 °C of the observed skin temperatures. It is concluded that the proposed models for the prediction of the mean skin temperature are valid for a wide range of warm and hot ambient conditions in steady-state conditions, including those of high radiation and high humidity.
Building and Environment, 2009
The performance of Stolwijk's 25-node thermal model of the human body was evaluated for the prediction of the skin temperature of a sedentary person in a thermal-transient state. The skin temperature calculated by the original Stolwijk model was compared to experimental data obtained systematically from a large number of subjects exposed to stepwise changes in environmental conditions, including neutral (29.4 C), low (19.5 C), and high (38.9 C) ambient temperatures. The results show that the original Stolwijk model accurately predicts both the absolute value and the tendency in the transient mean skin temperature. This suggests that the Stolwijk model is valid for the prediction of the transient mean skin temperature for the ''average'' person under low-activity conditions. Discrepancies are observed in the local skin temperature for some segments. However, these discrepancies can be significantly reduced through modification of the basal skin blood flow distributions and the distributions of vasoconstriction and workload in the model.
International Journal of Biometeorology, 2001
A mathematical model for predicting human thermal and regulatory responses in cold, cool, neutral, warm, and hot environments has been developed and validated. The multi-segmental passive system, which models the dynamic heat transport within the body and the heat exchange between body parts and the environment, is discussed elsewhere. This paper is concerned with the development of the active system, which simulates the regulatory responses of shivering, sweating, and peripheral vasomotion of unacclimatised subjects. Following a comprehensive literature review, 26 independent experiments were selected that were designed to provoke each of these responses in different circumstances. Regression analysis revealed that skin and head core temperature affect regulatory responses in a nonlinear fashion. A further signal, i.e. the rate of change of the mean skin temperature weighted by the skin temperature error signal, was identified as governing the dynamics of thermoregulatory processes in the cold. Verification and validation work was carried out using experimental data obtained from 90 exposures covering a range of steady and transient ambient temperatures between 5°C and 50°C and exercise intensities between 46 W/m 2 and 600 W/m 2 . Good general agreement with measured data was obtained for regulatory responses, internal temperatures, and the mean and local skin temperatures of unacclimatised humans for the whole spectrum of climatic conditions and for different activity levels.
Validation of a model for prediction of skin temperatures in footwear
Journal of physiological anthropology …, 2000
A model for foot skin temperature prediction was evaluated on the basis of 2 experiments on subjects at various environmental temperatures (light seated manual work at 10.7°C (Study 1), and a short walking period in combination with standing and sitting at +2.8°C, 11.8°C and 24.6°C (Study 2), with boots of 3 insulation levels. Insulation of the footwear was measured on a thermal foot model. Predicted and measured data showed a relatively good correlation (r=0.87) at the 2 colder conditions in Study 2. The environmental temperature of 2.8°C was not low enough at the chosen activity for a considerable foot skin temperature drop. In Study 1 the predicted temperature stayed higher for the whole exposure period and the difference between the predicted and the measured foot skin temperatures grew proportionally with time, while subsequent warmup curves at room temperature were almost parallel. In Study 1 the correlation was 0.95. However, the paired t-test showed usually significant differences between measured and predicted foot skin temperatures. The insulation values from thermal foot measurements can be used in the model calculations. Lotens' foot model is lacking activity as direct input parameter, however, the blood flow is used instead (effect through Tcore). The Lotens foot model can give reasonable foot skin temperature values if the model limitations are considered. Due to the lack of activity level input, it will be difficult to make any good estimation of foot skin temperature during intermittent exercise. The rate of the foot temperature recovery after cold exposure was somewhat overestimated in the model -the warmup of the feet of the subjects started later and was slower in the beginning of the warm-up than in the prediction. It could be useful to develop the model further by taking into consideration various wetness and activity levels.
Frontiers in Physiology, 2018
Non-invasive, multi-parameter methods to estimate core body temperature offer several advantages for monitoring thermal strain, although further work is required to identify the most relevant predictor measures. This study aimed to compare the validity of an existing and two novel multi-parameter rectal temperature prediction models. Thirteen healthy male participants (age 30.9 ± 5.4 years) performed two experimental sessions. The experimental procedure comprised 15 min baseline seated rest (23.2 ± 0.3 • C, 24.5 ± 1.6% relative humidity), followed by 15 min seated rest and cycling in a climatic chamber (35.4 ± 0.2 • C, 56.5 ± 3.9% relative humidity; to +1.5 • C or maximally 38.5 • C rectal temperature, duration 20-60 min), with a final 30 min seated rest outside the chamber. In session 1, participants exercised at 75% of their heart rate maximum (HR max) and wore light athletic clothing (t-shirt and shorts), while in session 2, participants exercised at 50% HR max, wearing protective firefighter clothing (jacket and trousers). The first new prediction model, comprising the input of 18 non-invasive measures, i.e., insulated and non-insulated skin temperature, heat flux, and heart rate ("Max-Input Model", standard error of the estimate [SEE] = 0.28 • C, R 2 = 0.70), did not exceed the predictive power of a previously reported model which included six measures and no insulated skin temperatures (SEE = 0.28 • C, R 2 = 0.71). Moreover, a second new prediction model that contained only the two most relevant parameters (heart rate and insulated skin temperature at the scapula) performed similarly ("Min-Input Model", SEE = 0.29, R 2 = 0.68). In conclusion, the "Min-Input Model" provided comparable validity and superior practicality (only two measurement parameters) for estimating rectal temperature versus two other models requiring six or more input measures.
A Thermal Skin Model for Comparing Contact Skin Temperature Sensors and Assessing Measurement Errors
Sensors (Basel, Switzerland), 2021
To improve the measurement and subsequent use of human skin temperature (Tsk) data, there is a need for practical methods to compare Tsk sensors and to quantify and better understand measurement error. We sought to develop, evaluate, and utilize a skin model with skin-like thermal properties as a tool for benchtop Tsk sensor comparisons and assessments of local temperature disturbance and sensor bias over a range of surface temperatures. Inter-sensor comparisons performed on the model were compared to measurements performed in vivo, where 14 adult males completed an experimental session involving rest and cycling exercise. Three types of Tsk sensors (two of them commercially available and one custom made) were investigated. Skin-model-derived inter-sensor differences were similar (within ±0.4 °C) to the human trial when comparing the two commercial Tsk sensors, but not for the custom Tsk sensor. Using the skin model, all surface Tsk sensors caused a local temperature disturbance wit...
Journal of Applied Physiology
[PDF] [Full Text] [Abstract] , September , 2011; 111 (3): 938-945. III under heat stress A physiological systems approach to modeling and resetting of mouse thermoregulation [PDF] [Abstract] , December , 2011; 81 (20): 2149-2159. Textile Research Journal protective clothing on wearer thermal balance Using a mathematical model of human temperature regulation to evaluate the impact of [PDF] [Full Text] [Abstract] , December 15, 2013; 115 (12): 1822-1837. J Appl Physiol A 3-D mathematical model to identify organ-specific risks in rats during thermal stress [PDF] [Full Text] [Abstract] , April 15, 2014; 306 (8): R552-R566. Am J Physiol Regul Integr Comp Physiol Yaroslav I. Molkov, Maria V. Zaretskaia and Dmitry V. Zaretsky Meth math: modeling temperature responses to methamphetamine including high resolution figures, can be found at: Updated information and services /content/87/5/1957.full.html can be found at: Journal of Applied Physiology about Additional material and information Fiala, Dusan, Kevin J. Lomas, and Martin Stohrer. A computer model of human thermoregulation for a wide range of environmental conditions: the passive system. J. Appl. Physiol. 87(5): 1957Physiol. 87(5): -1972Physiol. 87(5): , 1999.-A dynamic model predicting human thermal responses in cold, cool, neutral, warm, and hot environments is presented in a two-part study. This, the first paper, is concerned with aspects of the passive system: 1) modeling the human body, 2) modeling heattransport mechanisms within the body and at its periphery, and 3) the numerical procedure. A paper in preparation will describe the active system and compare the model predictions with experimental data and the predictions by other models. Here, emphasis is given to a detailed modeling of the heat exchange with the environment: local variations of surface convection, directional radiation exchange, evaporation and moisture collection at the skin, and the nonuniformity of clothing ensembles. Other thermal effects are also modeled: the impact of activity level on work efficacy and the change of the effective radiant body area with posture. A stable and accurate hybrid numerical scheme was used to solve the set of differential equations. Predictions of the passive system model are compared with available analytic solutions for cylinders and spheres and show good agreement and stable numerical behavior even for large time steps. dynamic simulation; human heat transfer; asymmetric thermal environments; exercise; numerical modeling 8750-7587/99 $5.00
Building and Environment, 2021
Human thermoregulation models, particularly the Fiala model, are well accepted for the prediction of thermoregulatory responses and the assessment of clothing and building systems with regard to human thermal comfort. However, the convective heat transfer coefficients (h c ), used for the heat transfer calculations, were obtained from measurements with a thermal manikin with poor body resolution from decades ago, and they were not distinguished between body postures. In this study, the overall and local h c of the human body in both standing and seated postures were evaluated by CFD simulations and implemented in the Fiala model to investigate the resultant influence on the predicted thermoregulatory responses by comparing the predictions with measurements of 14 human exposures. It was found that the original h c used in the Fiala model was similar to the simulated h c of the standing body, but higher than that of the seated body at most body parts. In 64% of the investigated human exposures, the root-mean-square-deviation of the skin temperature predicted by the Fiala model with the simulated h c was lower than that achieved with the original h c , indicating an improvement of the accuracy of the Fiala model. Additionally, the higher h c of the standing body resulted in a lower mean skin temperature by up to 1.5 • C when compared to the seated body in the environment of 10 • C and 2.5 m/s. This emphasises the necessity of ensuring the accuracy of the h c in the thermoregulation model in order to improve its validity for specific investigated conditions.
Building and Environment, 2019
Thermo-physiological modelling has become a frequently used and valuable tool for simulations of thermoregulatory responses in a variety of applications, such as building and vehicular comfort studies. To achieve reliable results, it is necessary to provide precise inputs, such as clothing thermal parameters. These values are usually presented in a standing body position and scarcely reported locally for individual body parts. Moreover, as an air gap distribution is both highly affected by a given body position and critical for clothing insulation, this needs to be taken into account. Therefore, the aim of this study was to examine eight probable approaches to assess the clothing parameters using stateof-the-art measurements, analytical and empirical models, and estimation. Next, we studied the effects of the eight clothing inputs on predicted thermo-physiological response under the same environmental conditions conducted with the Fiala model. Secondly, the study focuses on differences between seated and standing positions, both using two clothing sets representing typical European, indoor, summer and winter ensembles. The results show clear differences in clothing thermal properties between sitting and standing positions on both lower limbs and torso. The outputs of the eight
Prediction of human core body temperature using non-invasive measurement methods
International Journal of Biometeorology, 2014
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.