In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person's states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.
A two-level hierarchy of fuzzy logic to infer linguistic summarizations of voxel person's states, reconstructed from two cameras, is introduced for performing activity inference REF .
Linguistic summarization of video for fall detection using voxel person and fuzzy logic
Computer vision and image understanding : CVIU
Computer vision and image understanding : CVIU
Computer Science Medicine