  Thermal comfort is an assessment of one's satisfaction with the surroundings;
yet, most mechanisms that are used to provide thermal comfort are based on
approaches that preclude physiological, psychological, and personal
psychophysics that are precursors to thermal comfort. This leads to many people
feeling either cold or hot in an environment that was supposed to be thermally
comfortable to most users. To address this problem, this paper proposes to use
heart rate variability (HRV) as an alternative indicator of thermal comfort
status. Since HRV is linked to homeostasis, we conjectured that people's
thermal comfort could be more accurately estimated based on their heart rate
variability (HRV). To test our hypothesis, we analyzed statistical, spectral,
and nonlinear HRV indices of 17 human subjects doing light office work in a
cold, neutral, and hot environment. The resulting HRV indices were used as
inputs to machine learning classification algorithms. We observed that HRV is
distinctively different depending on the thermal environment and that it is
possible to reliably predict each subject's thermal state (cold, neutral, and
hot) with up to 93.7% accuracy. The result of this study suggests that it could
be possible to design automatic real-time thermal comfort controllers based on
people's HRV.
