Expert Perceptual Behavior under the Spatiotemporal Visual Constraints in Table Tennis

Modeling of SPM-GRU ping-pong ball trajectory prediction incorporating YOLOv4-Tiny algorithm

The research aims to lift the accuracy of table tennis trajectory prediction through advanced computer vision and deep learning techniques to achieve real-time and accurate table tennis ball position and motion trajectory tracking. The study concentrates on the innovative application of a micro-miniature fourth-generation real-time target detection algorithm with a gated loop unit to table tennis ball motion analysis by combining physical models and deep learning methods. The results show that in the comparison experiments, the improved micro-miniature fourth-generation real-time target detection algorithm outperforms the traditional target detection algorithm, with the loss value decreasing to 1.54. Its average accuracy in multi-target recognition is dramatically increased to 86.74%, which is 22.36% higher than the original model, and the ping-pong ball recognition experiments show that it has an excellent accuracy in various lighting conditions, especially in low light, with an average accuracy of 89.12%. Meanwhile, the improved model achieves a processing efficiency of 85 frames/s. In addition, compared with the traditional trajectory prediction model, the constructed model performs the best in table tennis ball trajectory prediction, with errors of 4.5 mm, 25.3 mm, and 35.58 mm. The results show that the research trajectory prediction model achieves significant results in accurately tracking table tennis ball positions and trajectories. It not only has practical application value for table tennis training and competition strategies, but also provides a useful reference for the similar techniques application in other sports.

Biomechanical Analysis of Different Footwork Foot Movements in Table Tennis

In order to solve the problem of athletes with lower limb movement injuries in table tennis footwork, with the increasing risk of sports, the incidence of acute injury also increased. The increase in athletic training, on the one hand, improves the skill level of the athlete; on the other hand, it increases the chances of more chronic injuries. Through the investigation of the injury site of 143 table tennis players, the researchers found that the lower limb injury accounted for 32% of the total injuries and the upper limb injury rate accounted for 33.14% of the trunk, accounting for 34.29%. The injury sites are mostly concentrated in the lumbar region, followed by the shoulders and knees. Through epidemiological research on the injuries of outstanding table tennis players, the survey results show that the probability of lower extremity injuries ranks in the top three, and most of them are acute sprains and chronic strain injuries. By applying the principles of sports biomechanics, a biomechanical analysis of the asynchronous foot movement in table tennis is proposed; from three aspects of kinematics, dynamics, and plantar pressure, it is found that the injury of table tennis is closely related to technical play connect. The risk of sports injury is inevitable in a sense, due to the long-term local overload, which causes the strain of the sports system. In order to avoid or reduce the occurrence of such sports injuries, coaches should standardize the technical movements of the players and arrange the exercise load reasonably according to the characteristics of the sports.