Innovative Application of Computer Vision and Motion Tracking Technology in Sports Training

Authors

  • Changqing Liu
  • Yanan Xie Hainan Tropical Ocean University

DOI:

https://doi.org/10.4108/eetpht.10.5763

Keywords:

AR, VR, Athletes, Sports

Abstract

The use of cutting-edge technology has resulted in a significant enhancement in athletic training. Computer vision and motion tracking are very important for enhancing performance, reducing the risk of accidents, and training in general. Some computer vision algorithms investigate how a sportsperson moves when competing or practising. It is possible that coaches who continuously evaluate their players’ posture, muscle activation, and joint angles would have a better understanding of biomechanical efficiency. It is possible to generate performance measurements from the real-time surveillance of athletes while competing in sports. Through the use of computer vision, it is possible to identify acts that might be hazardous. Notifications are given to coaches if there is a deviation in the form of an athlete, which enables them to address the situation as soon as possible. The three variables that these sensors monitor are the direction, speed, and acceleration. Athletes can encounter realistic environments thanks to the integration of motion tracking with virtual reality. One may use the feedback loop to increase their spatial awareness and decision-making ability. Augmented reality allows for enhancing an athlete’s eyesight by providing them with real-time data while practising. Last but not least, the use of computer vision and motion tracking is bringing about a significant improvement in the sporting training process. Through collaborative efforts, researchers, athletes, and coaches can accelerate humans' performance to levels that have never been seen before.

Downloads

Download data is not yet available.

Author Biography

Changqing Liu

School of Physical Education, Hainan Tropical Ocean University, Sanya 572000, China

References

Orunbayev, A. (2023). Globalisation and Sports Industry. American Journal Of Social Sciences And Humanity Research, 3(11), 164-182.

Host, K., & Ivašić-Kos, M. (2022). An overview of Human Action Recognition in sports based on Computer Vision. Heliyon, 8(6).

Ngugi, L. C., Abelwahab, M., & Abo-Zahhad, M. (2021). Recent advances in image processing techniques for automated leaf pest and disease recognition–A review. Information processing in agriculture, 8(1), 27-51.

Zhang, Z., Wen, F., Sun, Z., Guo, X., He, T., & Lee, C. (2022). Artificial intelligence‐enabled sensing technologies in the 5G/internet of things era: from virtual reality/augmented reality to the digital twin. Advanced Intelligent Systems, 4(7), 2100228.

Ding, Y., Li, Y., & Cheng, L. (2020). Application of Internet of Things and virtual reality technology in college physical education. Ieee Access, 8, 96065-96074.

Li, D., Yi, C., & Gu, Y. (2021). Research on college physical education and sports training based on virtual reality technology. Mathematical Problems in Engineering, 2021, 1-8.

Zhao, K., & Guo, X. (2022). Analysis of the application of virtual reality technology in football training. Journal of Sensors, 2022.

Iatsyshyn, A. V., Kovach, V. O., Romanenko, Y. O., Deinega, I. I., Iatsyshyn, A. V., Popov, O. O., ... & Lytvynova, S. H. (2020). Application of augmented reality technologies for preparation of specialists of new technological era.

Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., & Kim, T. K. (2021). Multiple object tracking: A literature review. Artificial intelligence, 293, 103448.

Ulrich, J., Blaha, J., Alsayed, A., Rouček, T., Arvin, F., & Krajník, T. (2023). Real Time Fiducial Marker Localisation System with Full 6 DOF Pose Estimation. ACM SIGAPP Applied Computing Review, 23(1), 20-35.

Ashtari, N., Bunt, A., McGrenere, J., Nebeling, M., & Chilana, P. K. (2020, April). Creating augmented and virtual reality applications: Current practices, challenges, and opportunities. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-13).

Asaad, R. R. (2021). Virtual reality and augmented reality technologies: A closer look. International Research Journal of Science, Technology, Education, & Management (IRJSTEM), 1(2).

Seçkin, A. Ç., Ateş, B., & Seçkin, M. (2023). Review on Wearable Technology in sports: Concepts, Challenges and opportunities. Applied Sciences, 13(18), 10399.

Soltani, P., & Morice, A. H. (2020). Augmented reality tools for sports education and training. Computers & Education, 155, 103923.

Lele, V. P., Kumari, S., & White, G. (2023). Streamlining Production: Using Big-Data’s CRM & Supply Chain To Improve Efficiency In High-Speed Environments. IJCSPUB-International Journal of Current Scienc (IJCSPUB), 13(2), 136-146.

Zhu, L. (2021). Computer vision-driven evaluation system for assisted decision-making in sports training. Wireless Communications and Mobile Computing, 2021, 1-7.

Wang, P. (2021). Research on sports training action recognition based on deep learning. Scientific Programming, 2021, 1-8.

Gao, N. (2021). Construction and simulation of athlete’s wrong action recognition model in sports training based on embedded wireless communication and computer vision. Wireless Communications and Mobile Computing, 2021, 1-11.

Du, W. (2024). The computer vision simulation of athlete’s wrong actions recognition model based on artificial intelligence. IEEE Access.

Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., ... & Cable, N. T. (2017). Monitoring athlete training loads: consensus statement. International journal of sports physiology and performance, 12(s2), S2-161.

Talha, M. (2022). Research on the use of 3D Modeling and motion capture technologies for making sports training easier. Revista de Psicología del Deporte (Journal of Sport Psychology), 31(3), 1-10.

Liu, J., Wang, L., & Zhou, H. (2021). The application of human–computer interaction technology fused with artificial intelligence in sports moving target detection education for college athlete. Frontiers in Psychology, 12, 677590.

Taha, Z., Hassan, M. S. S., Yap, H. J., & Yeo, W. K. (2016). Preliminary investigation of an innovative digital motion analysis device for badminton athlete performance evaluation. Procedia engineering, 147, 461-465.

Naik, B. T., Hashmi, M. F., & Bokde, N. D. (2022). A comprehensive review of computer vision in sports: Open issues, future trends and research directions. Applied Sciences, 12(9), 4429.

Host, K., & Ivašić-Kos, M. (2022). An overview of Human Action Recognition in sports based on Computer Vision. Heliyon, 8(6).

Downloads

Published

24-04-2024

How to Cite

1.
Changqing Liu, Xie Y. Innovative Application of Computer Vision and Motion Tracking Technology in Sports Training. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Apr. 24 [cited 2024 May 4];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5763