# A New Approach to Fall Detection Based on the Human Torso Motion Model
## 2 Related Work  
With the development of machine learning, computer vision and image processing techniques, the video camera or computer vision-based method has become a new, but hot topic of fall detection. Compared with the wearable device-based approaches, the camera or computer vision-based methods are less intrusive, more accurate and robust. Moreover, the detection systems can be easily updated by downloading the newest programs. One typical fall detection method identified and located the person in the video by using a vertical projection histogram with a statistical scheme. Then, the ratio of the width and height of the bounding box and the absolute difference between these two values were used to detect falls [16]. In [17], the authors divided the image into many rectangular areas, then falls can be detected by calculating the area of the body. [PERSON] et al. [18] presented a fuzzy system that used the accelerometer and the Kinect sensor to detect the fall event. However, since the human external rectangle is difficult to detect accurately, false alarms and missed detections are the major problems of this method. Video sequence analysis is another major branch of vision-based fall detection. [PERSON] and [PERSON] [19] used motion history images and detected large motion in a video sequence. The combination of the aspect ratio of the bounding box and the changing rate of width and height were used in their approach to detect falls. In [20], overlapping smart cameras were used for fall detection and localization. In [21],a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. Fall detection is realized by analyzing the postures that are extracted from video clips. [PERSON] et al. [22] proposed a new way to track 3D head trajectory by only one calibrated camera. The vertical velocity of the 3D head is used to judge whether a fall happens. [PERSON] et al. [23] used optical flow to detect falls and recognize other human activities. Recently, depth images have been widely used for action recognition and classification, because of the particular advantages in privacy protection and the availability of being easily collected by Kinect or other sensors. [PERSON] et al. [24] proposed a fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images captured by the Kinect sensor. [PERSON], [PERSON] et al. [25] used several cameras to create 3D vision. A measure of the vertical distribution along the vertical axis is calculated, and a fall event is detected when this distribution is abnormally near the ground for a certain length of time. [PERSON] et al. [26] proposed a fall detection method for indoor environments, based on the usage of the Kinect depth sensor in an \"on-ceiling\" configuration and on the analysis of depth frames. [PERSON] et al. [27] proposed a lightweight approach to recognize gestures with Kinect by utilizing approximate string matching. [PERSON] et al. [28] fetch data from the depth image and detect falls by measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. [PERSON] et al. [29] presented a 3D path planning system that uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. [PERSON]. [30] built a view-invariant descriptor for human activity representation by 3D skeleton joint positions to detect falls and other activities. However, all of these methods have shortcomings. For example, in [31], the ratio of the height and width of the rectangle extracted from a person was used to determine whether a person falls. This method uses three features, i.e., human aspect ratio, effective area ratio and center variation rate.