Low-power and efficient ambient assistive care system for elders

This paper presents a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. We build a probabilistic spatial map of resting locations using the head position of the subject, represented as cluster centres discovered by K-means in the camera view space. A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints.