Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables.
Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables.
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DOI：
10.1038/s41597-022-01347-w
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发表时间：
2022-06-02
期刊：
影响因子：
9.8
通讯作者：
Salim, Flora D.
中科院分区：
文献类型：
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作者：
Gao, Nan;Marschall, Max;Burry, Jane;Watkins, Simon;Salim, Flora D.
我们在澳大利亚墨尔本郊区的一所K-12私立学校进行了实地研究。数据采集包含两个要素：第一，为期5个月的纵向实地研究In-Gauge使用两个室外气象站，以及17个教室的室内气象站和居住者控制的房间空调通风口上的温度传感器;这些数据以5分钟的记录频率整理成每个教室的单独数据集，包括关于居住者存在的其他数据。该数据集用于推导居住者如何操作房间空调机组的预测模型。其次，我们在为期4周的横断面研究En-Gage中跟踪了23名学生和6名教师，使用可穿戴传感器记录生理数据，以及每日调查以查询居住者的热舒适度，学习参与度，情绪和座位行为。总体而言，综合数据集可用于分析室内/室外气候与学生在校园内的行为/精神状态之间的关系，这为未来设计智能反馈系统提供了机会，使学生和员工受益。
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants’ thermal comfort, learning engagement, emotions and seating behaviours. Overall, the combined dataset could be used to analyse the relationships between indoor/outdoor climates and students’ behaviours/mental states on campus, which provide opportunities for the future design of intelligent feedback systems to benefit both students and staff.
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