Embedded system for fall detection using body-worn accelerometer and depth sensor

This paper presents an embedded system for fall detection using accelerometric data and depth maps. A real-time processing of motion data and depth maps is realized on a low-cost PandaBoard platform. In order to achieve detection of human falls with low computational cost the system performs a depth-based inferring about the fall event when person´s movement is above some preset threshold. The performance of the system has been evaluated on our publicly available dataset consisting of synchronized depth maps and motion data. To investigate the detection accuracy in depth maps from different camera views the image sequences were simultaneously recorded by two Kinect sensors, where one of them was placed in the front of the scene, whereas the second one was located on the ceiling. The motion data were acquired by a body-worn accelerometer and transmitted wirelessly to the processing unit, responsible for both synchronization and recording or processing of the data.