Understanding Indoor Context in an Office Environment: An Empirical Study on Air Stuffiness Perception

Indoor environments are an essential part of the everyday context for everyone, influencing the health, well-being, and performance outcomes of occupants. Stuffy indoor air is a common problem caused by various factors, such as poor ventilation, occupant behaviour, and pollutants, which present challenges for automatic detection. Serving as an example study to understand the indoor context in an office environment, this study predicted perceived air stuffiness using multi-domain sensor data and self-reported labels from 23 knowledge workers, each for periods ranging from 3.5 to 7 months in natural settings. The study evaluated the applicability of three supervised machine learning methods: support vector, logistic regression, and random forest, in classifying knowledge workers' perceptions of air stuffiness at both personal and room levels. The results showed that machine learning models tailored to individuals and specific office rooms can predict 'stuffy' and 'not stuffy' perceptions, with random forest achieving the highest average accuracy of 69.6% for person-specific models and logistics regression achieving the highest average accuracy of 66.7^% for room-specific models. The results suggest that a data-driven approach can help forecast perceived air stuffiness and potentially be used to optimise the indoor context to better support well-being and productivity in workplaces.