Occupancy Detection with Environmental Sensors Using Motion Sensors as Proxy Labels

Occupancy detection and activity recognition are crucial for building automation and patient monitoring. Traditional motion sensors have limitations, such as binary output and the inability to detect stationary individuals, compromising detection accuracy. This study explores environmental sensors - such as ambient light, temperature, humidity, and sound levels - as alternatives or complements to motion sensors for occupancy detection. Using the “Smart Home environment data across 4 European countries”, compromising various sensor configurations in individual rooms, we utilize event-based sensors (primarily motion sensors) as proxy labels for presence. We train both generalized and personalized XGBoost models: the generalized model predicts occupancy in new environments, while the personalized model is tailored to individuals. Comparing model performance, we find that personalized models generally outperform generalized ones, though the best configurations' F1-scores are close (0.77 vs. 0.76). The sound level features are the most influential in both models, indicating that environmental sensors, particularly with sound data, can enhance occupancy detection and potentially reduce the dependence on motion sensors. This study improves our understanding of the importance and configurations of sensors for occupancy detection and highlights the advantages of personalized models in smart home applications.