An analysis on human fall detection using skeleton from Microsoft Kinect

In this paper, we present a novel fall detection system based on the Kinect sensor. The originalities of this system are two-fold. Firstly, based on the observation that using all joints to represent human posture is not pertinent and robust because in several human postures the Kinect is not able to track correctly all joints, we define and compute three features (distance, angle, velocity) on only several important joints. Secondly, in order to distinguish fall with other activities such as lying, we propose to use Support Vector Machine technique. In order to analyze the robustness of the proposed features and joints for fall detection, we have performed intensive experiments on 108 videos of 9 activities (4 falls, 2 falls like and 3 daily activities). The experimental results show that the proposed system is capable of detecting falls accurately and robustly.