Year: 2025
Type: conference-paper
Abstract: Due to the non Gaussian dynamic coupling characteristics of sports basketball trajectories, traditional result based statistical data is difficult to reflect the constraints and blocking relationships of different basketball teaching tasks during the execution process. Unable to make matching teaching adjustments based on these sports data, resulting in ineffective improvement of basketball shooting accuracy in student sports training. In this regard, the application method of IoT technology in the visual design of sports basketball courses has been proposed. The study utilized the perception layer of the Internet of Things to obtain motion measurement data from different students, and integrated multi-sensor data from video surveillance and wearable devices to reduce basketball visual trajectory data errors through D-S evidence decision rules based on multi-source fusion technology. Considering the non Gaussian dynamic coupling characteristics of basketball trajectory data, constraints and blocking relationships for different visual teaching tasks were generated using comprehensive feature parameters. By quantifying the impact of different teaching tasks on basketball trajectory based on priority, we can optimize and match basketball teaching tasks to improve basketball shooting accuracy. Generate visual effects of students' comprehensive sports data to achieve visual design of sports basketball courses. The experimental results show that after applying the designed sports basketball course visualization method, students' shooting accuracy within the three-point line is significantly improved, effectively improving their shooting accuracy.
Authors: Kexiong Cui, Yongji Shen
Institution: Sanya Aviation and Tourism College
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411744585