Abstract
The purposes are to digitalize and intellectualize current professional sports training and enrich the application scenarios of motion capture technology of moving targets based on artificial intelligence (AI) and human–computer interaction (HCI) in sports training. From an educational psychology perspective, sport techniques are a cognitive ability of sports, and a tacit knowledge. However, sports technology, language, image, and other methods play an auxiliary role in sports training. Here, a General Framework of Knowledge-Based Coaching System (KBCS) is proposed using the HCI technology and sports knowledge to accomplish autonomous and intelligent sports training. Then, the KBCS is applied to table tennis training. The athletic performance is evaluated quantitatively through the calculation of the sports features, motion recognition, and the hitting stage division in table tennis. Results demonstrate that the speed calculated by the position after mosaicking has better continuity after the initial frame of the unmarked segment is compared with the end frame of the market segment. The typical serve and return trajectories in three serving modes of slight-spin, top-spin, and back-spin, as well as the trajectories of common services and return errors, are obtained through the judgment of the serving and receiving of table tennis. Comparison results prove that the serving accuracy of slight-spin and back-spin is better than that of top-spin, and a lower serve speed has higher accuracy. Experimental results show that the level distribution of the three participants calculated by the system is consistent with the actual situation in terms of the quality of the ball returned and the standard of the motion, proving that the proposed KBCS and algorithm are useful in a small sample, thereby further improving the accuracy of pose restoration of athletes in sports training.
Highlights
Nowadays, sports have been vigorously promoted by all social spheres and are closely related to the daily life and social activities of people
If the time difference between the two frames of data is very small, and the speed calculated by the position has good continuity after mosaicking, it is considered that the two segments correspond to the flight trajectory of the same table tennis, and they are mosaicked
Experimental results show that the level distribution of the three participants calculated by the system is consistent with the actual situation in terms of the quality of the ball returned and the standard of the motion, proving that the proposed Knowledge-Based Coaching System (KBCS) and algorithm are useful in a small sample, laying a solid foundation for comprehensive and accurate sports evaluation and sports feedback in the future
Summary
Sports have been vigorously promoted by all social spheres and are closely related to the daily life and social activities of people. With the surging sports population, professional coaches are severely out of demand, so some fitness people cannot get in-depth guidance. Professional sport has entered the era of digital competition ahead of time, and the era of solely relying on the talent of athletes and the experience of coaches has gradually faded out (Mazzeo et al, 2018). Athletic abilities include professional skills, professional intelligence, physical fitness, and psychological quality. For excellent athletes whose intelligence and professional skills have reached a fairly high level, the impact of physical fitness on their sports career may be crucial (Liu and Zhu, 2020). Physical exercise is a very complex system, whose reasonable and controllable engineering depends on digital scientific monitoring (Lee and Han, 2019).
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