Automatic detection of indoor air pollution-related activities using metal-oxide gas sensors and the temporal intrinsic dimensionality estimation of data

Abstract

Ensuring indoor air quality (IAQ) is crucial for safeguarding health, with daily occupant activities serving as significant sources of pollutants. This study addresses the need to identify and mitigate indoor pollution events caused by activities like cleaning and cooking. Employing metal-oxide gas (MOX) sensors, we propose a method that automatically detects indoor air pollution-related activities through intrinsic dimensionality estimation on time-windowed multivariate signals. The approach was validated using a dataset derived from two months of experiments involving 10 common household activities in a 13 m² (46 m³) room, utilizing 21 distinct MOX sensor references. The dataset, which included labeled activities, demonstrated the method’s superior accuracy compared to existing literature, showcasing its robustness against sensor drift. This research contributes to raising awareness, enabling timely intervention, and facilitating the automation of smart ventilation systems to maintain healthy indoor environments.