A new approach to fall detection based on the human torso motion model

Abstract

This paper presents a new approach for fall detection based on two features and their motion characteristics extracted from the human torso. The 3D positions of the hip center joint and the shoulder center joint in depth images are used to build a fall detection model named the human torso motion model (HTMM). Person's torso angle and centroid height are imported as key features in HTMM. Once a person comes into the scene, the positions of these two joints are fetched to calculate the person's torso angle. Whenever the angle is larger than a given threshold, the changing rates of the torso angle and the centroid height are recorded frame by frame in a given period of time. A fall can be identified when the above two changing rates reach the thresholds. By using the new feature, falls can be accurately and effectively distinguished from other fall-like activities, which are very difficult for other computer vision-based approaches to differentiate. Experiment results show that our approach achieved a detection accuracy of 97.5%, 98% true positive rate (TPR) and 97% true negative rate (TNR). Furthermore, the approach is time efficient and robust because of only calculating the changing rate of gravity and centroid height.