A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Abstract

Computer-aided sports systems are an important area of current research. To be specific, it can combine computer vision, computer graphics, and motion capture technologies with the characteristics of sports, and design and develop assisted systems according to user needs. As a result, it is of great practical importance and application to improve the entertainment and science of sports learning, to stimulate public participation in sports, and to learn sports skills. Table tennis has a very great popular base in China and has a wide range of audiences. However, for beginners or amateurs of table tennis, it is difficult to learn table tennis skills by relying on books, videos, etc., due to the lack of professional technical guidance, which makes it relatively difficult for them to continuously improve their skills. Sometimes they tend to form wrong technical habits, which are not easy to detect and correct in time, and can lead to sports injuries over time. Therefore, the lack of professional instruction has hindered the development of table tennis in public fitness to a certain extent. On the other hand, in a table tennis course, the learning of table tennis techniques is mostly dependent on the teacher’s explanation. Students are bored with the repetitive and boring teaching because they need to repeat the exercises several times. The formation of correct table tennis movements requires repeated instruction from the instructor to address problems with the student’s movements. However, due to limited teaching resources, it is difficult for teachers to accommodate different students during the teaching process. In addition, with a limited team of instructors, the level of proficiency varies, making it difficult for them to make an objective assessment. As a result, with the rapid development and application of modern science and technology in the field of sports, there is an urgent need to explore intelligent sports technology instructional support systems to improve the shortcomings of the traditional teaching model. Motion recognition technology, as an essential branch of multimodal interaction technology, has been developing rapidly in recent years. This research focus on designing and implementing a system for recognizing human motion based on skeletal point pose information by combining inertial sensors. This system can collect data from sensors located at the main skeletal points of the human body and transmits them to the host computer through multi-Bluetooth pairing transmission. After that, support vector machines are applied to classify human movements and to recognize general human movements. This system has significant advantages for human movement recognition and classification due to the distinctive technical characteristics of table tennis. The recognition and classification of both players in the video can play an important role in the technical analysis and tactical arrangement of the players. As a result, the table tennis motion correction system based on human motion feature recognition has crucial research significance and application value.