Extension of a soft-computing framework for activity analysis from linguistic summarizations of video

Video cameras are a relatively low-cost, rich source of information that can be used for ldquowell-beingrdquo assessment and abnormal event detection for the goal of allowing elders to live longer and healthier independent lives. We previously reported a soft-computing fall detection system, based on two levels from a hierarchy of fuzzy inference using linguistic summarizations of activity acquired temporally from a three dimensional voxel representation of the human found by back projecting silhouettes acquired from multiple cameras. This framework is extremely flexible and rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. In this paper, we show the flexibility of our activity analysis framework by extending it to additional common elderly activities and contextual awareness is added for reasoning based on location or static objects in the apartment.