Application of Sanda-Assisted Teaching System Integrating VR Technology from a 5G Perspective

Since the focus of Sanda teaching is to allow students to master martial arts techniques through confrontational exercises, Sanda teaching in colleges and universities commonly adopts contextualized teaching methods. However, this Sanda teaching method suffers from deficiencies such as poor teaching effectiveness and difficulty in reflecting the effectiveness of Sanda martial arts. In order to solve these problems and make up for the shortcomings of offline Sanda teaching, the study adopts the virtual reality technology and the fifth generation mobile communication technology to construct a Sanda-assisted teaching system for college students. In order to ascertain whether students complete the Sanda movement practice, the study has designed two models: a self-supervised model based on acceleration and angular velocity contrast learning and a multi-task semi-supervised model based on time-frequency contrast learning. These models aim to improve the analytical function of the Sanda-assisted teaching system and address the analytical deficiencies of the existing human movement identification algorithms. The results indicated that the maximum accuracy of the research-designed self-supervised model was 95.76% and 95.89% on the training and test sets, respectively. The multi-task semi-supervised model designed in the study plateaued after nearly 22 and 24 iterations on the training and test sets, respectively. The average response time of the research-designed system was 59ms, and the throughput could reach a maximum of 77651bit/s. The model and the research-designed system both worked well, and they can lower the risk of student injuries while offering technological support for Sanda-assisted teaching and learning in higher education institutions.

@article{Zhang2024,
  title     = {Application of Sanda-Assisted Teaching System Integrating VR Technology from a 5G Perspective},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Zhaoquan Zhang and Yong Ding},
  doi       = {10.14569/IJACSA.2024.01508108},
  url       = {https://doi.org/10.14569/IJACSA.2024.01508108}
}