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Personal thermal comfort models with wearable sensors

机译：带有可穿戴传感器的个人热舒适模型

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A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at least 20 h per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24%/78%/0.79 (Cohen's kappa/ accuracy/AUC) with all features considered. The median prediction power reaches 21%/71%/0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35%/76%/0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success.
机译：个人舒适度模型是用于热舒适性建模的一种方法，用于热环境设计和控制，可预测个人的热舒适性响应，而不是大量人口的平均响应。我们使用实验室级可穿戴设备在日常日常活动中开发了个人热舒适模型。我们收集了2-4周（每天至少20小时）的14位受试者（6位女性和8位男性成年人）的生理信号（例如，皮肤温度，心率）和环境参数（例如，气温，相对湿度）。然后，我们使用不同的机器学习算法为每个主题训练了14个模型，以预测其热偏好。结果表明，在考虑所有特征的情况下，中值预测能力可能高达24％/ 78％/ 0.79（科恩kappa /准确性/ AUC）。经过200次主观投票后，预测中位数达到21％/ 71％/ 0.7。通过考虑一个数据集中的所有主题，我们探索了不同功能对预测性能的重要性。当包含整个数据集的所有功能时，个人舒适度模型可以通过最具预测性的算法生成35％/ 76％/ 0.80的最高性能。当乘员的热感超出热中性时，个人舒适度模型将显示最高的预测能力。在脚踝处测量的皮肤温度比在手腕处测量的皮肤温度更具预测性。我们建议应采用Cohen的kappa或AUC来评估不平衡数据集的个人热舒适模型的性能，因为它具有排除随机成功的能力。

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