Biomechanical research based on the recognition and detection of the strength of table tennis hitting action
Abstract
The reason why China’s table tennis can continue to this day is because it is an important heritage sports event in the country. In order to ensure the high-level performance of the Chinese table tennis team in the game, the identification and detection of its sports characteristics is a very meaningful work. With the development of computer technology, the use of multimedia intelligence technology to conduct research on competitive tactics has become a general consensus in the sports world. This paper explores the recognition and detection technology of the strength of table tennis hitting movements from the perspective of biomechanics, aiming to provide scientific basis and technical support for improving the performance of athletes. Based on the principles of biomechanics and combined with multimedia intelligent algorithms, this study developed a spatiotemporal graph convolutional network (STGCN) and a motion detection method based on Kinect technology to identify and quantify the hitting strength of table tennis players. The experimental results of this paper show that in the recognition of different types of movements based on the STGCN method, the correct recognition rate of 100 groups of movement strength is 84%, and the correct recognition rate of 500 groups of movement strength is 91.6%; in the recognition of different types of movement strength based on Kinect, the correct recognition rate of 100 groups of movement strength is 97%, and the correct recognition rate of 500 groups of movement strength is 99.6%; it can be seen that no matter how many groups of hitting movements are made, the correct recognition rate of strength based on Kinect is higher than that of STGCN.
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