Predicting thermal pleasure experienced in dynamic environments from simulated cutaneous thermoreceptor activity.
alliesthesia
dynamic environments
machine learning
pleasure
predictive model
thermal physiology
thermoreceptor
Journal
Indoor air
ISSN: 1600-0668
Titre abrégé: Indoor Air
Pays: England
ID NLM: 9423515
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
revised:
26
04
2021
received:
15
03
2021
accepted:
06
05
2021
pubmed:
29
5
2021
medline:
9
11
2021
entrez:
28
5
2021
Statut:
ppublish
Résumé
Research into human thermal perception indoors has focused on "neutrality" under steady-state conditions. Recent interest in thermal alliesthesia has highlighted the hedonic dimension of our thermal world that has been largely overlooked by science. Here, we show the activity of sensory neurons can predict thermal pleasure under dynamic exposures. A numerical model of cutaneous thermoreceptors was applied to skin temperature measurements from 12 human subjects. A random forest model trained on simulated thermoreceptor impulses could classify pleasure responses (F1 score of 67%) with low false positives/negatives (4%). Accuracy increased (83%) when excluding the few extreme (dis)pleasure responses. Validation on an independent dataset confirmed model reliability. This is the first empirical demonstration of the relationship between thermoreceptors and pleasure arising from thermal stimuli. Insights into the neurophysiology of thermal perception can enhance the experience of built environments through designs that promote sensory excitation instead of neutrality.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2266-2280Informations de copyright
© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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