A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor
A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor
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DOI：
10.1016/j.buildenv.2019.106223
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发表时间：
2019-08-01
影响因子：
7.4
通讯作者：
Becerik-Gerber, Burcin
中科院分区：
文献类型：
--
作者：
Aryal, Ashrant;Becerik-Gerber, Burcin
物联网和机器学习的最新进展为大规模部署传感器以监测环境并在个人层面上对热舒适性进行建模和预测提供了可能性。使用从可穿戴设备或热成像获得的生理信息来提高个人热舒适性预测的兴趣越来越大。在这项研究中，我们比较了使用环境传感器和空气温度传感器、使用生理传感器和手腕佩戴的设备监测手腕皮肤温度或使用热像仪监测面部皮肤温度预测个人热感觉和满意度的准确性。这项实验是在受控环境中进行的，没有任何辐射热源或局部舒适设备；只是改变了气温。对于所研究的条件，我们的结果表明，与单独使用生理传感器(可穿戴设备或热像仪)相比，使用环境传感器的数据来预测热舒适性会产生更高的精度。将环境传感器和生理传感器的数据结合在一起，比只使用环境传感器的准确率高出约3%-4%。生理传感器的精度略有提高可能不足以证明大规模使用生理传感器来预测没有辐射热源或局部舒适设备的环境中的热舒适性的隐私问题和额外成本是合理的。未来需要在人口较多的不同环境条件下进行研究，以更好地了解在个人水平上预测热舒适的不同传感方法之间的权衡。
Recent advancements in Internet of Things and Machine Learning have opened the possibility of deploying sensors at a large scale to monitor the environment and to model and predict thermal comfort at an individual level. There has been a growing interest to use physiological information obtained from wearable devices or thermal imaging to improve individual thermal comfort prediction. In this study, we compared the accuracies of using environmental sensing with an air temperature sensor, physiological sensing with a wrist-worn device to monitor wrist skin temperature or thermal camera to monitor facial skin temperatures for predicting individual thermal sensation and satisfaction. The experiment was conducted in a controlled environment without any radiant heat sources or local comfort devices; solely the air temperature was changed. For the conditions studied, our results indicate that using data from an environmental sensor for predicting thermal comfort results in a higher accuracy compared to using physiological sensors (either wearable device or thermal camera) alone. Combining data from both environmental and physiological sensors leads to about 3%-4% higher accuracy than using environmental sensors only. Slight improvement in accuracy from the physiological sensors might not be sufficient to justify the privacy concerns and additional costs of using physiological sensors at a large scale for predicting thermal comfort in environments without radiant heat sources or local comfort devices. Future studies under different environmental conditions with a larger population are needed to better understand the tradeoffs between different sensing methods for predicting thermal comfort at an individual level.