Application of core strength training based on learning control robot technology in Badminton

  • Li Wu Sports Academy, Xinyang Agriculture and Forestry University, Xinyang 464000, Henan, China
  • Xin Feng Humanities Institute, Zhaoqing Medical College, Zhaoqing 526070, Guangdong, China
Keywords: core strength training; learning controlled robotics; Badminton training; special sports qualities
Ariticle ID: 88

Abstract

The implementation of core strength training in badminton has received increasing attention as the game and training theories have matured. A single typical strength training session is not the same as core strength training. Strengthening the trunk’s core muscles can help athletes become more adaptable and physically strong, which will have a significant influence on badminton’s future growth. At present, core strength training has been tried to be applied to the routine training of athletes. However, its application has not been scientifically guided and its role cannot be fully exerted, which has adversely affected the improvement of athletes’ skill levels. Consequently, a thorough analysis of badminton’s features was done in this research. The use of core strength training in badminton was thoroughly investigated based on an exploration of the application value of this technique in conjunction with learning and control robot technology. This article examined three levels of special abilities, physical fitness and balancing ability, and compared it with the standard core strength training technology to demonstrate the application impact of the learning control robot’s core strength training in badminton. According to the experimental findings, learning control robot technology may be achieved through core strength training, the average scores of students’ badminton speed quality and strength quality were about 8.10 and 8.45. There was a clear difference between the scores of 6.69 and 7.54 of the traditional training methods, which proved its feasibility.

References

1. Amjad MK, Butt SI, Anjum N, et al. A layered genetic algorithm with iterative diversification for optimization of flexible job shop scheduling problems. Advances in Production Engineering & Management. 2020; 15(4): 377–389. doi: 10.14743/apem2020.4.372

2. Chai J, Liu P, Wang Q. Research and Analysis of Badminton Training Strategy Based on Computer Intelligent Control. Revista de la Facultad de Ingenieria. 2017; 32(9): 30–37.

3. Chin NS, Ting KY. Effect of Eight-week Plyometric and Resistance Band Training on Badminton Overhead Clear Stroke in 12 Years Old Players. Pertanika Journal of Social Science and Humanities. 2019; 27(S3): 183–192.

4. Darzabi T, Taheri HR, Saberi Kakhki AR. The Variation of Acquisition, Consolidation Memory and Coordination Pattern of Elbow Joint in Short Service Badminton before and after Aerobic Training. Annals of Applied Sport Science. 2018; 6(1): 37–46. doi: 10.29252/aassjournal.6.1.37

5. Fatih YÜKSEL M. Examination of Reaction Times of Elite Physically Disabled Badminton Players. International journal of Science Culture and Sport. 2017; 5(25): 319–327. doi: 10.14486/intjscs695

6. Frank S. Cleaning Filling Lines and Tanks with Robot Technology. Brauwelt international. 2017; 35(2): 137–139.

7. Huang Q, Shi Y. Analysis and research on training mode optimization of badminton players based on data mining technology. Revista de la Facultad de Ingenieria. 2017; 32(16): 294–300.

8. Huanpin L. Research and application of Outward Bound Training in badminton sports. Agro Food Industry Hi Tech. 2017; 28(1): 1548–1552.

9. Javaid M, Haleem A, Singh RP, et al. Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics. 2021; 1: 58–75. doi: 10.1016/j.cogr.2021.06.001

10. Jiang H, Zhang H, Xie X, et al. Neural-network-based learning algorithms for cooperative games of discrete-time multi-player systems with control constraints via adaptive dynamic programming. Neurocomputing. 2019; 344: 13–19. doi: 10.1016/j.neucom.2018.02.107

11. Kumari R, Jeong JY, Lee BH, et al. Topic modelling and social network analysis of publications and patents in humanoid robot technology. Journal of Information Science. 2019; 47(5): 658–676. doi: 10.1177/0165551519887878

12. Liu Y, Han J, Xiao L. Analysis of badminton training strategy based on computer intelligent control. BoletinTecnico/Technical Bulletin. 2017; 55(20): 289–295

13. Nassar B, Hussein W, Medhat M. Supervised learning algorithms for spacecraft attitude determination and control system health monitoring. IEEE Aerospace and Electronic Systems Magazine. 2017; 32(4): 26–39. doi: 10.1109/maes.2017.150049

14. Nirendan J, Murugavel DrK. Effect of shadow training on motor fitness components of badminton players. International Journal of Physiology, Sports and Physical Education. 2019; 1(2): 04–06. doi: 10.33545/26647710.2019.v1.i2a.8

15. Nugroho S, Nasrulloh A, Karyono TH. Effect of intensity and interval levels of trapping circuit training on the physical condition of badminton players. Journal of Physical Education and Sport. 2021; 21(3): 1981–1987.

16. Ren JF, Ye CM, Li Y. A new solution to distributed permutation flow shop scheduling problem based on NASH Q-Learning. Advances in Production Engineering & Management. 2021; 16(3): 269–284. doi: 10.14743/apem2021.3.399

17. Tsumaki Y, Mori K. Robot Technology Challenging the Mysteries of Animals. Journal of the Robotics Society of Japan. 2017; 35(6): 463–466. doi: 10.7210/jrsj.35.463

18. Van Riet TCT, Chin Jen Sem KTH, Ho JPTF, et al. Robot technology in dentistry, part one of a systematic review: literature characteristics. Dental Materials. 2021; 37(8): 1217–1226. doi: 10.1016/j.dental.2021.06.001

19. Vázquez-Canteli JR, Nagy Z. Reinforcement learning for demand response: A review of algorithms and modeling techniques. Applied Energy. 2019; 235: 1072–1089. doi: 10.1016/j.apenergy.2018.11.002

20. Wei X. An analysis of the training mode of badminton players using data mining technology. Agro Food Industry Hi Tech.2017; 28(1): 3486–3489.

21. Xu X. Research on Teaching Reform of Robot Technology under the Background of New Engineering. Open Access Library Journal. 2020; 07(12): 1–7. doi: 10.4236/oalib.1107005.

Published
2024-09-02
How to Cite
Wu, L., & Feng, X. (2024). Application of core strength training based on learning control robot technology in Badminton. Molecular & Cellular Biomechanics, 21, 88. https://doi.org/10.62617/mcb.v21.88
Section
Article