# Exploration of Applying Pose Estimation Techniques in Table Tennis
## 1 Introduction  
Sports science is a cross-disciplinary applied science, and its research and development results and benefits can be directly applied to the public; it can have a deep and specific impact in competitive sports, national health, preventive medicine, or enhancing the economic development of the sports industry [1]. The Ministry of Science and Technology of Taiwan [2] has noted that the sports industry is already mature in Europe and the U.S., with significant economic scale and benefits, and that the annual growth rate of the globalsports industry is estimated to exceed 6%. However, compared with the relatively well-developed European and American countries, there is still considerable room for growth in the Asian sports industry and market. The future of the sports industry can be further developed through close cooperation with sports science research.  
The integration of computer vision in artificial intelligence (AI) technology into sports training and competition is important for two reasons. One is the application of AI to bring sports viewing to a new level of development and enhance the visual perception of sports viewing. Second, the application of gesture recognition in technical analysis to improve the competitiveness of the game is a new trend in the development of technology integration in sports. With the emphasis on precision sports research and academic research, the application of \"training\" and \"tactical information collection\" has become crucial. With the rapid advancement of technology, the importance of sports technology in assisting with accurate scientific research is growing. The rapid development of wearable devices in recent years has resulted in a boom in applications in sports fields due to an increase in the accuracy and miniaturization of sensor elements. The sensor or wearable device is used to detect sports posture, and the acceleration information and three-axis sensor information of the sensor are collected to analyze the athlete's movement [3]. However, the limitation is that it must be worn; the requirement to wear the relevant device results in some differences from the real sports situation, and due to device limitations and costs, some sports items or situations will be difficult to popularize and apply. Nevertheless, with the development of posture recognition technology in computer vision, if the athletes' posture in the video can be directly analyzed through the computer, benefits of real-time, noncontact analysis can be achieved. When analyzing the posture, the athletes do not need to wear any device, the situation is the same as the competition situation, and the video of the competition can be analyzed afterward. Therefore, introducing this technology will reduce the cost and achieve popularization.  
Accurate real-time recognition of sports posture will greatly benefit the information collection at the \"training level\" for posture correction, and at the \"competition level\" for analysis of technical and tactical use. Therefore, technology-assisted precision sports research has become a crucial research issue. Technology can be introduced and integrated into sports technology and assistance in a timely manner, which will enhance sports and improve the national sports culture and athletic strength. The purposes of this study were to analyze the applications and limitations of pose estimation/recognition for motion analysis in computer vision in AI. This study also discusses past research, practical applications, limitations, and future trends of pose recognition technology.