## 1 Introduction  
As the number of elderly people in the world continues to rise and the problem of population aging becomes more and more serious, falls are increasingly threatening the lives and health of the elderly [1, 2, 3], and an elderly person falls have become an important part of medical care. The physical functions of the elderly over 65 years old are significantly reduced, and at the same time, their reaction ability and balance ability are very weak, and they can easily fall. Once an elderly person falls, the degree of injury of the elderly may be more serious if no one else finds or rescues them, the old person's injury may be more serious, and they may even fall into a coma, endangering their life. In addition, different fall postures during a fall will cause different impact positions, causing varying degrees of injury to the elderly [4, 5]. Therefore, there is a need for an automatic detection method, which cannot only recognize the fall behavior of the elderly in time, but also recognize the fall posture, so that the nursing staff can grasp the specific details such as the impact position and the degree of injury of the elderly, and provide the elderly with more targeted rescue and treatment. In recent years, artificial intelligence technology is developing rapidly. Its application in fall detection has become more and more extensive. Many experts and scholars carried out research work on fall detection and posture recognition. According to the different fall detection equipment, the current fall detection technology can be divided into video, surrounding environment, and wearable fall detection. The method based on video image collects human motion images by deploying one or more video cameras in the user's active area and analyzes the real-time collected video images based on the characteristics of the fall motion image extracted offline to determine whether the current user has fallen or recognize the posture of a fall [6; 7; 8; 9; 10; 11]. The method based on the surrounding environment uses infrared, pressure, and audio sensors arranged in the surrounding environment (walls, floors, ceilings, carpets) to obtain data, analyze, and recognize human behavior [12; 13]. The above two methods have the advantages of high accuracy and no need to wear detection equipment, but the whole system is very complicated, incurring high costs and a large amount of data calculation, which easily exposes personal privacy. Moreover, outdoor monitoring cannot be achieved. With the help of commonly used mobile smart terminals and wearable devices, methods based on wearable devices are widely used to recognize fall postures. Their advantages in terms of accuracy and real-time recognition have made them a research hotspot in the field of healthcare. In this method, a microsensor is worn on the human body to collect data, and a back-end algorithm is used to recognize the falling postures. Most of the studies used acceleration or gyroscope data time series, statistical domain or transform domain features, and used curve similarity comparison or pattern recognition algorithms for fall detection [14; 15; 16; 17; 18]. A small set of two or three uniaxial accelerometers mounted on the body were first utilized to distinguish several static and dynamic activities (standing, sitting, lying, walking, ascending stairs, descending stairs, cycling) [19]. [PERSON] et al. [20] proposed to install an acceleration sensor device on the limbs and crotch and use the support vector machine (SVM) algorithm to judge a variety of typical actions such as jumping, standing still, walking, running, which achieves a detection effect of 92.25% accuracy. [PERSON] et al. [21] proposed a method of using a mobile phone as a carrier and embedding a three-dimensional acceleration sensor into it to monitor the body's daily motion posture, covering running, sitting, rising, falling, and other actions, with an accuracy rate of 97.7%. [PERSON] et al.