# Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables

arXiv: https://arxiv.org/abs/2105.06637 · HTML: https://mlsanity.com/paper/2105.06637/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 7 of 7)

- [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](https://mlsanity.com/paper/2005.04120/) (2020)
- [Transfer Learning for Thermal Comfort Prediction in Multiple Cities](https://mlsanity.com/paper/2004.14382/) (2020)
- [ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data](https://mlsanity.com/paper/2008.03230/) (2020)
- [Predicting Personality Traits from Physical Activity Intensity](https://mlsanity.com/paper/1906.07864/) (2019)
- [An Ambient-Physical System to Infer Concentration in Open-plan Workplace](https://mlsanity.com/paper/2005.13535/) (2020)
- [n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild](https://mlsanity.com/paper/2007.04831/) (2020)
- [Seating preference analysis for hybrid workplaces](https://mlsanity.com/paper/2007.15807/) (2020)

## Built on by (most cited first, 4 of 4)

- [GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization](https://mlsanity.com/paper/2211.02733/) (2022)
- [BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics](https://mlsanity.com/paper/2406.08990/) (2024)
- [Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities](https://mlsanity.com/paper/2407.14501/) (2024)
- [Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning](https://mlsanity.com/paper/2311.04215/) (2023)

## Abstract (arXiv)

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.