An Automated Active Vision System for Fall Detection and Posture Analysis in Ambient Assisted Living Applications

The paper presents an active vision system for the detection of dangerous fall events and the recognition of four main human postures (lie, sit, stand, bend) in Ambient Assisted Living applications. The suggested vision system uses a Time-Of-Flight camera providing accurate 3D measurements of the scene in all illumination conditions. In order to accommodate different installation setups, the system recovers automatically the own 3D position and orientation in the space, according to a floor detection strategy, without human intervention and calibration tools (landmarks, patterns, etc.). The moving people are detected in the 3D points cloud by applying segmentation/tracking methods and metric filtering. The distance of the 3D human centroid from the floor plane is evaluated by using the previously estimated calibration parameters and the corresponding trend is used as feature in a thresholding-based clustering for fall detection. The system shows high performances in terms of efficiency and reliability on a large real dataset of falls acquired in different conditions. The posture recognition is carried out by using both the 3D human centroid distance from the floor plane and the orientation of the body torso estimated by applying a topological approach to the range images. Experimental results on synthetic data validate the soundness of the proposed posture recognition approach.