Context-Aided Occupancy Detection and Tracking Using Networked SLEEPIR Sensors

Accurate occupancy detection remains a challenging problem due to dynamic occupancy patterns and varying environments. Traditional machine learning (ML) struggles with this variability as models typically require large datasets and frequent updates as occupancy scenarios are unlimited and continuously change over time. It is thus infeasible to train a single "universal" model for the diverse real-world scenarios given real-world computational constraints. To address these issues, a context-aware hierarchical classification framework is proposed which periodically trains multiple occupancy classifiers on subsets of data delineated by meaningful contexts. When new occupancy data arrive, its context is identified, and a corresponding pretrained classifier is selected for prediction. By focusing each model on more consistent data distributions defined by context, this approach aims to improve classification accuracy compared to baselines trained on static datasets alone. The framework also aims to eliminate the need for offline training on large datasets and frequent overthe-cloud model updates required by traditional ML approaches by performing ML-based training and inference directly on the sensor node via an Internet-of-Things (IoTs) device. The framework is evaluated via datasets collected both in an office and a residential setting, monitored by a network of synchronized low-energy electronically chopped passive infra-red (SLEEPIR) sensors. These sensors, unlike conventional passive infrared (PIR) sensors, can detect stationary occupants. Time-series features are extracted from observations and clustered to discover underlying contextual scenarios. Experimentation resulted in context scenarios which essentially represent varying levels of infrared (IR) noise in observed environment. The proposed framework achieved a 5.03% accuracy improvement over the best baseline algorithm.