  Thermal comfort is a personal assessment of one's satisfaction with the
surroundings. Yet, most thermal comfort delivery mechanisms preclude
physiological and psychological precursors to thermal comfort. Accordingly,
many people feel either cold or hot in an environment that is supposedly
thermally comfortable to most people. To address this issue, this paper
proposes to use people's heart rate variability (HRV) as an alternative
indicator of thermal comfort. Since HRV is linked to homeostasis, we
hypothesize that it could be used to predict people's thermal comfort status.
To test our hypothesis, we analyzed statistical, spectral, and nonlinear HRV
indices of 17 human subjects doing light office work in a cold, a neutral, and
a hot environment. The resulting HRV indices were used as inputs to machine
learning classification algorithms. We observed that HRV is distinctively
altered depending on the thermal environment and that it is possible to
steadfastly predict each subject's thermal environment (cold, neutral, and hot)
with up to a 93.7% prediction accuracy. The result of this study implies that
it could be possible to design automatic real-Time thermal comfort controllers
based on people's HRV.

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