Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables
This work addresses the problem of optimizing indoor environments for comfort and engagement in educational settings, but it is incremental as it builds on existing sensor-based monitoring approaches without introducing new methods.
The study tackled the problem of understanding how indoor climate and occupant behavior interact in a school setting, resulting in a combined dataset from sensors and wearables that can predict air-conditioning usage and analyze relationships between climate and students' mental states.
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.