Literature DB >> 22106946

Thermal sensation: a mathematical model based on neurophysiology.

B R M Kingma1, L Schellen, A J H Frijns, W D van Marken Lichtenbelt.   

Abstract

UNLABELLED: Thermal sensation has a large influence on thermal comfort, which is an important parameter for building performance. Understanding of thermal sensation may benefit from incorporating the physiology of thermal reception. The main issue is that humans do not sense temperature directly; the information is coded into neural discharge rates. This manuscript describes the development of a mathematical model of thermal sensation based on the neurophysiology of thermal reception. Experimental data from two independent studies were used to develop and validate the model. In both studies, skin and core temperature were measured. Thermal sensation votes were asked on the seven-point ASHRAE thermal sensation scale. For the development dataset, young adult males (N=12, 0.04Clo) were exposed to transient conditions; Tair 30-20-35-30°C. For validation, young adult males (N=8, 1.0Clo) were exposed to transient conditions; Tair: 17-25-17°C. The neurophysiological model significantly predicted thermal sensation for the development dataset (r2=0.89, P<0.001). Only information from warm-sensitive skin and core thermoreceptors was required. Validation revealed that the model predicted thermal sensation within acceptable range (root mean squared residual=0.38). The neurophysiological model captured the dynamics of thermal sensation. Therefore, the neurophysiological model of thermal sensation can be of great value in the design of high-performance buildings. PRACTICAL IMPLICATIONS: The presented method, based on neurophysiology, can be highly beneficial for predicting thermal sensation under complex environments with respect to transient environments.
© 2011 John Wiley & Sons A/S.

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Year:  2011        PMID: 22106946     DOI: 10.1111/j.1600-0668.2011.00758.x

Source DB:  PubMed          Journal:  Indoor Air        ISSN: 0905-6947            Impact factor:   5.770


  5 in total

1.  Meth math: modeling temperature responses to methamphetamine.

Authors:  Yaroslav I Molkov; Maria V Zaretskaia; Dmitry V Zaretsky
Journal:  Am J Physiol Regul Integr Comp Physiol       Date:  2014-02-05       Impact factor: 3.619

2.  A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation.

Authors:  Shih-Lung Pao; Shin-Yu Wu; Jing-Min Liang; Ing-Jer Huang; Lan-Yuen Guo; Wen-Lan Wu; Yang-Guang Liu; Shy-Her Nian
Journal:  Int J Environ Res Public Health       Date:  2022-06-14       Impact factor: 4.614

3.  A Coupled Phase-Temperature Model for Dynamics of Transient Neuronal Signal in Mammals Cold Receptor.

Authors:  Firman Ahmad Kirana; Husin Alatas; Irzaman Sulaiman Husein
Journal:  J Biophys       Date:  2016-09-28

4.  Differences between the most used equations in BAT-human studies to estimate parameters of skin temperature in young lean men.

Authors:  Borja Martinez-Tellez; Guillermo Sanchez-Delgado; Francisco M Acosta; Juan M A Alcantara; Mariëtte R Boon; Patrick C N Rensen; Jonatan R Ruiz
Journal:  Sci Rep       Date:  2017-09-05       Impact factor: 4.379

5.  Cognitive Appraisals Affect Both Embodiment of Thermal Sensation and Its Mapping to Thermal Evaluation.

Authors:  Trevor P Keeling; Etienne B Roesch; Derek Clements-Croome
Journal:  Front Psychol       Date:  2016-06-27
  5 in total

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