Predicting thermal pleasure experienced in dynamic environments from simulated cutaneous thermoreceptor activity

Thomas Parkinson, Hui Zhang, Ed Arens, Yingdong He, Richard de Dear*, John Elson, Alex Parkinson, Clay Maranville, Andrew Wang

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)2266-2280
    Number of pages15
    JournalIndoor Air
    Volume31
    Issue number6
    DOIs
    Publication statusPublished - Nov 2021

    Keywords

    • alliesthesia
    • dynamic environments
    • machine learning
    • pleasure
    • predictive model
    • thermal physiology
    • thermoreceptor

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