Biomechanical insights and optimization in the teaching design of badminton games based on motion capture and adaptive virtual reality video coding
Abstract
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.
References
1. Saleem N, Gao J, Khattak MI, Rauf HT, Kadry S, ShafiM, DeepResGRU: Residual gated recurrent neural network-augmented Kalman filtering for speech enhancement and recognition. KBS. 2021; 238:107914.1-107914.13.
2. Wang X, Zhang P, Gao W, Li Y, Wang Y, Pang H. Misfire Detection Using Crank Speed and Long Short-Term Memory Recurrent Neural Network. Energies. 2022;15(1):300-310.
3. Lee C, Ng K, Chen C, Lau H, Chung S, Tsoi T. American Sign Language Recognition and Training Method with Recurrent Neural Network. Expert Syst. Appl. 2021;167(1):114403.1-114403.14.
4. Mekruksavanich S, Jitpattanakul A. Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data. Electronics. 2021;10(14):1685-1685.
5. Simonetto M, Arena S, Peron M.A methodological framework to integrate motion capture system and virtual reality for assembly system 4.0 workplace design Safety Sci. 2022;146(2):105561.1-105561.14.
6. Maciejewski M, Piszczek M, Pomianek M, Norbert P. Design and Evaluation of A Steamvr Tracker For Training Applications - Simulations and Measurements. MetrolMeas Syst. 2020;27(4):601-614.
7. Zhuo N, Sharma A. 3D Visual Motion Amplitude Tracking Simulation Method for Sports. Recent AdvElectr El. 2021;14(7):718-726.
8. Kadirvelu B, Gavriel C, Nageshwaran S, Chan J P, Nethisinghe S, Athanasopoulos S, Ricotti V, Voit T, Giunti P, Festenstein R, Faisal AA. A wearable motion capture suit and machine learning predict disease progression in Friedreich’s ataxia. Nature medicine. 2023, 29(1): 86-94.
9. Obukhov A D, Volkov A A, Vekhteva N A, Patutin K I, Nazarova A O, Dedov D L. The method of forming a digital shadow of the human movement process based on the combination of motion capture systems. Informatics and automation. 2023, 22(1): 168-189.
10. Ning B, Na L. Deep Spatial/temporal-level feature engineering for Tennis-based action recognition, FCGS. 2021;152(6):188-193.
11. Cai J, Hu J, Tang X, Hung T,Tan Y. Deep Historical Long Short-Term Memorys for Action Recognition. Neurocomputing. 2020;407(24):428-438.
12. Hu X, Bao X, Xie S, Wei G. Unsupervised motion capture data segmentation based on topic model. ComputAnimatVirt W. 2020;32(1):2-12.
13. Buchman-Pearle J, Acker S. Estimating Soft Tissue Artifact of the Thigh in High Knee Flexion Tasks Using Optical Motion Capture: Implications for Marker Cluster Placement. J Biomech. 2021;127(8):5-17.
14. Uslu T, Gezgin E, Zbek S, Guzin D, Etin L. Utilization of Low Cost Motion Capture Cameras for Virtual Navigation Procedures: Performance Evaluation for Surgical Navigation. Measurement. 2021;181(5):109624.
15. Vlahek D, Stoi T, Golob T, Milos K, Teja L, Matjaz V, Domen M. Method for estimating tensiomyography parameters from motion capture data. SAI. 2021;45(2):213-222.
16. Min H, Chen Z, Fang B, Xia Z, Song Y, Wang Z, Zhou Q, Sun F, Liu C. Cross-Individual Gesture Recognition Based on Long Short-Term Memory Networks. Sci Program. 2021; 5:6680417.1-6680417.11.
17. Xia Z, Xing J, Wang C, Li X. Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network. MIS. 2021; 2:2621691.1-2621691.12.
18. Venkatnarayan R, Mahmood S, Shahzad M. WiFi based Multi-User Gesture Recognition. IEEE Trans Mob Comput. 2021;20(3):1242-1256.
19. Wonderen EV, Unsworth S. Testing the validity of the Cross-Linguistic Lexical Task as a measure of language proficiency in bilingual children. J Child Lang. 2021;48(6):1101-1125.
20. Lee W, Chun J, Lee Y, Park K, Park S. Contrastive learning for knowledge tracing//Proceedings of the ACM Web Conference. 2022;2330-2338.
21. Mohammed S A, Al-Haddad L A, Alawee W H, Dhahad H A, Jaber A A, Al-Haddad S A. Forecasting the productivity of a solar distiller enhanced with an inclined absorber plate using stochastic gradient descent in artificial neural networks. Multiscale and Multidisciplinary Modeling, Experiments and Design. 2024, 7(3): 1819-1829.
22. Maurer M N. Correlates of early handwriting: Differential patterns for girls and boys. Early Education and Development. 2024, 35(4): 843-858.
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
