Biomechanical insights and optimization in the teaching design of badminton games based on motion capture and adaptive virtual reality video coding

The application of games in sports not only brings new development to sports, but also brings new requirements to sports. To maintain and enhance students’ learning motivation and interest, more effective individualized teaching is needed. This study focuses on students’ individualized knowledge structure, tracking the differences in sports skills among different types of students, and designing a sports game model based on sports games. A Bayesian network model was used to model learners’ knowledge and establish an adaptive badminton competition mode. Then, a new feature intersection method is studied, and a method for deep knowledge tracking is established using feature embedding and attention mechanisms. Finally, on this basis, this method is combined with adaptive learning methods to establish a badminton game model based on adaptive learning and improved deep knowledge tracking. Additionally, this study explores the biological principles underlying sports skill learning. When students learn badminton skills. When students learn badminton skills, the body’s proprioceptive system constantly provides feedback. This biological feedback is vital as it helps students adjust their movements unconsciously. Motion capture technology can capture the kinematic data of students’ badminton movements, such as joint angles and limb velocities. By integrating this data with the biological feedback, we can optimize learning outcomes by enabling more precise identification of skill deficiencies and more effective remediation. The experiment shows that the acceleration Z is the maximum, approximately 100. The acceleration of X is the smallest, approximately between −200 and 200. The most unstable is the acceleration Y. After positive compensation, the value of the adaptive quantization parameter cascade algorithm increases. And the quantized adaptive quantization parameter cascade algorithm value does not have a significant impact on the evaluation of the reconstructed image. The average values of each scale and sub dimension are above 0.70, and the constituent reliability values are above 0.90, indicating that the internal quality of each scale is good. And the internal consistency between questions is also good, all of which have passed the validity test. The survey method used in this experiment has strong practicality and can effectively achieve game design, which has great practical value in teaching practice.