Human activity analysis for in-home fall risk assessment

The aim of this work is to define a fall detection video system for indoor environments based on a RGB-D sensor and a low power and low cost embedded system that processes the sensor data in order to provide a description of human activities in the field of the Ambient Assisted Living. The RGB image is affected by a high luminescence sensibility, so the depth data have the aim to improve the human activity recognition. The system is usable in a sufficiently small room and it requires a RGB-D sensor located in the center of the ceiling and an embedded system connected on a computer network. The embedded system controls the RGB-D sensor and, in the mean time, classifies the images using computer vision algorithms based on the depth map. “Water Filling” algorithm or “Multi-Level Segmentation” algorithm are used to detect person. For each person, the system detects the position with respect to the room, estimating also the human posture. Among the features extracted we enumerate the height, the head size and the distance between the head and the shoulders. The system tracks a person through the frames starting from the first identification. Further, group interactions are monitored and analyzed. The posture detection algorithm takes into account the distance between the person head and the floor during the time. During the experimental phase, conducted in many domestic scenarios, the effectiveness of the proposed solution has been proved, that is fast, accurate and ables to provide a fall map in-home fall risk assessment.