摘要翻译：
热舒适性是个人对周围环境满意度的评估。然而，大多数热舒适传递机制排除了热舒适的生理和心理前兆。因此，许多人在一个对大多数人来说热舒适的环境中感到冷或热。为了解决这一问题，本文提出将人的心率变异性(HRV)作为热舒适性的替代指标。由于HRV与人体动态平衡有关，我们假设它可以用来预测人们的热舒适状况。为了验证我们的假设，我们分析了17名在寒冷、中立和炎热环境中从事轻度办公室工作的人类受试者的统计、光谱和非线性HRV指数。得到的HRV指数作为机器学习分类算法的输入。我们观察到HRV随热环境的变化而明显改变，并且可以稳定地预测每个受试者的热环境（冷、中性和热），预测准确率高达93.7%。研究结果表明，基于人体HRV的自动实时热舒适控制器的设计是可能的。
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英文标题：
《Heart Rate Variability as an Indicator of Thermal Comfort State》
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作者：
Kizito Nkurikiyeyezu, Yuta Suzuki, Yoshito Tobe, Guillaume Lopez,
  Kiyoshi Itao
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  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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PDF链接：
https://arxiv.org/pdf/2002.07480