# A New Approach to Fall Detection Based on the Human Torso Motion Model
## 1 Introduction  
Falls are one of the major health hazards among the aging population aged over 60. According to the report of the World Health Organization, approximately 28-35% of people aged 65 and over fall each year and 32-42% for those over 70 years of age. In fact, falls exponentially increase due to age-related biological changes, which lead to a high incidence of falls and fall-related injuries in ageing societies [1]. Nowadays, falls are not only life threatening, but also one of the major issues in elderly health. A fall can cause severe consequences, including broken bones, superficial cuts and abrasions to the skin soft tissue [2, 3]. If a falling person cannot get help in a short period of time, the situation will be even worse. As many people in this ageing group live alone, it is difficult for them to seek help immediately. For these and many other reasons, the number of research works on fall detection has increased dramatically over recent years. Automatic detection of human falls provides help to reduce the time between the fall and the arrival of medical attention [4, 5]. During recent years, different kinds of approaches have been proposed to realize automatic fall detection. [PERSON] et al. [6] classified the fall detection methods into three major categories: the wearable device-based method, the ambient sensor-based method and the vision-based method. Wearable sensors detect the motion and location of the human body. [PERSON] et al. [7; 8] adopted accelerations as the major feature to identify falls. [PERSON]. [9] judged falls by checking periods of inactivity. [PERSON] et al. [10] identified a fall by analyzing the posture of the person. Although these approaches did work in detecting falls, the disadvantages are also obvious. The wearable sensors can be worn or damaged due to external factors, and they will not detect falls if the person forgets to wear them. Besides, wearable devices may make people feel uncomfortable. Another type of approach uses sensors that are deployed in the environment to detect falls. [PERSON], [PERSON] et al. [11] checked whether there was a fall by collecting sounds, while [PERSON] et al. [12] detected falls by monitoring vibrations in a room. In [13], the authors focus on detecting falls in the bathroom by using smart floors to collect floor pressure information. Although the ambient sensor-based method is a creative way for fall detection, the accuracy and false alarm rate are unacceptable in most situations. Moreover, this kind of operation is limited to those places where the sensors have been deployed. Due to these main drawbacks of the above two approaches, the computer vision-based method has become a hot topic in recent research. The method needs no wearable devices and aims at detecting falls by analyzing video streams from one or more cameras. Compared with other approaches, this kind of method has more advantages. However, privacy is a problem for the people who live under the supervision. [PERSON] et al. [14] provided solutions for privacy identification and protection. In addition, [PERSON], [PERSON] et al. [15] defined five specific visualization levels corresponding to five visualization models, respectively. The appearance of persons can be protected at each level to a different degree. Our proposed method is based on computer vision, and the usage of only depth images helps maintain privacy. Compared with the method proposed in [15], the usage of depth images reaches the third visualization level. More detailed information about the typical methods based on computer vision technology is discussed in Section 2. The remaining part of this paper is organized as follows. Section 2 gives an overview of the current state-of-the-art with regard to features and methods used in the vision-based fall detection method. Section 3 describes our model and algorithm of fall detection. Section 4 shows the experiment and gives the results of our approach. Section 5 lists three major limitations of our model. Finally, we present our concluding remarks in Section 5.