Addressing Realisms in Activity Recognition for Smart Home Deployments

Research in wireless sensor networks has been very successful in creating test beds and short-term deployments for many application areas (e.g, home health care, saving energy in buildings, security systems) that depend on accurate activity recognition. The utility of these activity recognition systems often depends on recognizing anomalies from typical behaviors learned based on the activities. However, for many real home situations these activity recognition and anomaly detection solutions are not robust enough due to many realities.

In this dissertation, we have designed, implemented and evaluated a novel activity recognition system named AALO, a comprehensive anomaly detection system in daily activities named Holmes, and a novel ground truth collection system named Vocal-Diary that can be used to evaluate both AALO and Holmes. AALO is an active learning based activity recognition system that applies machine learning and data mining techniques to address some of the realities of deployments including di