Detecting human falls with 3-axis accelerometer and depth sensor

Previous work demonstrated that Kinect sensor can be very useful for fall detection. In this work we present a novel approach to fall detection that allows us to achieve reliable fall detection in larger areas through person detection and tracking in dense depth map sequences acquired by an active pan-tilt 3D camera. We demonstrate that both high sensitivity and specificity can be obtained using dense depth images acquired by a ceiling mounted Kinect and executing the proposed algorithms for lying pose detection and motion analysis. The person is extracted using depth region growing and person detection.