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

In this paper we present efficient and effective algorithms for fall detection on the basis of sequences of depth maps and data from a wireless inertial sensor worn by a monitored person. A set of descriptors is discussed to permit distinguishing between accidental falls and activities of daily living. Experimental validation is carried out on freely available dataset consisting of synchronized depth and accelerometric data. Extensive experiments are conducted in the scenario with a static camera facing the scene and an active camera observing the same scene from above. Several experiments consisting in person detection, tracking and fall detection in real-time are carried out to show efficiency and reliability of the proposed solutions. The experimental results show that the developed algorithms for fall detection have high sensitivity and specificity.