Design of Basketball Teaching Aid System Based on Artificial Intelligence

Abstract

Abstract Artificial intelligence has penetrated every aspect of people’s lives with the rapid development of science and technology. In basketball, how to effectively utilize artificial intelligence technology to improve the effectiveness of training has become a problem worth studying. A Mediapipe-based 3D human posture domain adaptive detection method is proposed in the study by combining statistics and human limb proportion simulation. The modified SVM classification algorithm is used to compare and classify the newly acquired key point data information of the system with the standard basketball action key point data information preset by the system to judge whether the basketball action is up to the standard or not, so as to design the basketball teaching aid system based on artificial intelligence. Based on this basis, after testing the recognition performance of the system, its practical application effect is explored. The total basketball scores improved after the experiment (86.26±2.228 for the experimental group and 81.34±9.672 for the control group), but there was no significant difference (P>0.05). The control group was slower than the experimental group by 0.64 seconds and 0.77 seconds overall in the cross-step breakout time with the ball and the same-side step breakout time with the ball, respectively, indicating that the experimental group was more proficient in basketball technology. The system has been tested in various ways to meet the multiple needs of the users, meet the expected standards, and be able to be applied in real-world scenarios.