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Lifestyle pattern extraction

The motion sensor technology and the network of cameras have been explored separately as attractive solutions for building in-home monitoring systems for elderly care. Each of these technologies offers advantages over others while suffering from certain limitations. Motion detectors usually offer a privacy preserving solution, but do not yield granular information about the user’s activities. Cameras, on the other hand, offer access to details of activities of daily life, but are regarded with caution in terms of coping with user privacy concerns. In this chapter, we provide an informative and a highly updated review of sensor fusion approaches. Then, we introduce an in-home monitoring system for elderly care, which is based on information collected from a network of PIR motion detection sensors and low-resolution cameras with 30 × 30 pixel arrays. The data fusion method we used for that system is based on different activity features. From the PIR sensors, the level of the daily occupation and the active level of the occupant are extracted. From the low-resolution cameras, other features such as activeness, sleep duration, visitors, and TV activity are extracted. Generally speaking, the proposed monitoring system fuses the heterogeneous sensor information to identify the occupant’s lifestyle pattern. The system employs K-means clustering algorithm to group the observed days into different lifestyle patterns such as restful isolated days, restful social days, busy isolated days, and busy social days. To evaluate our system, experiments were conducted on six months of real-data. The results show promising performance to identify a certain lifestyle pattern or changes which occur over time.