Study on the impact of shoulder flexibility training on smash speed mechanics in badminton using machine learning
Abstract
The smash stroke in badminton is a key attack style that makes the opponent player miss the strike; the smash requires speed, agility, strength, and precision. The smash demands a high level of shoulder flexibility from the players, which increases the Range of Motion (ROM) during the backswing and forward swing phases. The shoulder flexibility provides excellent energy storage and transfer, improving smash speed. The biomechanical efficiency of Shoulder Flexibility Training (FLT) on smash speed efficiency is still under study. This lack of study leads to modelling training program limitations, which may increase the risk of injury. Examining the process by which smash speed mechanics are impacted by Shoulder Flexibility Training (SFT) programs is the primary goal of the present investigation. It is approximately a 6-week training program for Amateur Players (AP) and National Players (NP), which uses core shoulder motions like flexion, abduction, and rotation as its basis. Motion capture systems and radar sensors investigated joint motion and smash speeds. To address the shortage of study evidence on the subject, a hybrid CNN + LSTM model was applied to predict smash speed concerning improved shoulder flexibility. As reported by the research results, students’ smash speed and shoulder flexibility improved significantly during training. There was a 4.35% boost to smash speed at contact and a 4.69% gain in shoulder internal rotation compared to non-contact athletes. Additionally, there was a 9.83% boost in smash speed at contact and a 9.76% boost in shoulder internal rotation for the best athletes. Considering post-training illnesses, the CNN + LSTM model successfully predicted smash speed, with R³ scores of 0.99 for NP and 0.97 for AP.
References
1. Ramasamy Y, Usman J, Sundar V, et al. Kinetic and kinematic determinants of shuttlecock speed in the forehand jump smash performed by elite male Malaysian badminton players. Sports Biomechanics. 2021; 23(5): 582-597. doi: 10.1080/14763141.2021.1877336
2. Li S, Zhang Z, Wan B, et al. The relevance of body positioning and its training effect on badminton smash. Journal of Sports Sciences. 2016; 35(4): 310-316. doi: 10.1080/02640414.2016.1164332
3. Barnamehei H, Tabatabai Ghomsheh F, Safar Cherati A, et al. Muscle and joint force dependence of scaling and skill level of athletes in high-speed overhead task: Musculoskeletal simulation study. Informatics in Medicine Unlocked. 2020; 20: 100415. doi: 10.1016/j.imu.2020.100415
4. Lenkova R, Lukacova T, Przednowek K. Assessment of kinematic parameters in three variants of crossminton serving: a comprehensive analysis of upper body movements. Journal of Physical Education and Sport. 2023; 23(10): 2652-2659.
5. Pardiwala DN, Subbiah K, Rao N, et al. Badminton Injuries in Elite Athletes: A Review of Epidemiology and Biomechanics. Indian Journal of Orthopaedics. 2020; 54(3): 237-245. doi: 10.1007/s43465-020-00054-1
6. Phomsoupha M, Laffaye G. Injuries in badminton: A review. Science & Sports. 2020; 35(4): 189-199. doi: 10.1016/j.scispo.2020.01.002
7. Drigny J, Guermont H, Reboursière E, et al. Shoulder Rotational Strength and Range of Motion in Unilateral and Bilateral Overhead Elite Athletes. Journal of Sport Rehabilitation. 2022; 31(8): 963-970. doi: 10.1123/jsr.2021-0342
8. Tengku Kamalden TF, Gasibat Q, et al. Occurrence of Muscle Imbalance and Risk of Injuries in Athletes using Overhead Movements: A Systematic Review. Sport Mont. 2021; 19(3): 115-122. doi: 10.26773/smj.211012
9. Wörner EA, & Safran MR. (2022). Specific Sports-Related Injuries. Cham: Springer International Publishing; 2022. pp. 431-446.
10. Li F, Li S, Zhang X, et al. Biomechanical Insights for Developing Evidence-Based Training Programs: Unveiling the Kinematic Secrets of the Overhead Forehand Smash in Badminton through Novice-Skilled Player Comparison. Applied Sciences. 2023; 13(22): 12488. doi: 10.3390/app132212488
11. He Z, Liu G, Zhang B, et al. Impact of specialized fatigue and backhand smash on the ankle biomechanics of female badminton players. Scientific Reports. 2024; 14(1). doi: 10.1038/s41598-024-61141-z
12. Kaur K, Rakheja H, & Anand P. Determination Of Combined Functional And Core Training Among Badminton And Tennis Players-A Systematic Review. Journal of Advanced Zoology. 2024; 45(1).
13. Hu Z, Kim Y, Dong T, et al. Correlation between gluteus muscle activity and dynamic control of the knee joint in a single-leg landing task in badminton. International Journal of Performance Analysis in Sport. 2023; 23(6): 429-440. doi: 10.1080/24748668.2023.2249760
14. Gonsalves A. Bilateral Muscular Asymmetry in Badminton Athletes. San Diego State University; 2023.
15. Calderón-Díaz M, Silvestre Aguirre R, Vásconez JP, et al. Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis. Sensors. 2023; 24(1): 119. doi: 10.3390/s24010119
16. Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review. Journal of Biomedical Physics and Engineering. 2023; 13(5). doi: 10.31661/jbpe.v0i0.2305-1621
17. Stetter BJ, & Stein T. (2024). Artificial Intelligence in Sports, Movement, and Health. Cham: Springer Nature Switzerland; 2024. pp. 139-160.
18. Ghezelbash F, Hossein Eskandari A, Robert-Lachaine X, et al. Machine learning applications in spine biomechanics. Journal of Biomechanics. 2024; 166: 111967. doi: 10.1016/j.jbiomech.2024.111967
19. Liew BXW, Pfisterer F, Rügamer D, et al. Strategies to optimise machine learning classification performance when using biomechanical features. Journal of Biomechanics. 2024; 165: 111998. doi: 10.1016/j.jbiomech.2024.111998
20. Mubarrat ST, Chowdhury S. Convolutional LSTM: a deep learning approach to predict shoulder joint reaction forces. Computer Methods in Biomechanics and Biomedical Engineering. 2022; 26(1): 65-77. doi: 10.1080/10255842.2022.2045974
21. Xiang L, Gu Y, Gao Z, et al. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors. Computers in Biology and Medicine. 2024; 170: 108016. doi: 10.1016/j.compbiomed.2024.108016
22. Liu K, Liu Y, Ji S, et al. Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System. Sensors. 2024; 24(3): 1032. doi: 10.3390/s24031032
23. Zhu M, Guan X, Li Z, et al. sEMG-Based Lower Limb Motion Prediction Using CNN-LSTM with Improved PCA Optimization Algorithm. Journal of Bionic Engineering. 2022; 20(2): 612-627. doi: 10.1007/s42235-022-00280-3
24. Li M, Wang J, Yang S, et al. A CNN-LSTM model for six human ankle movements classification on different loads. Frontiers in Human Neuroscience. 2023; 17. doi: 10.3389/fnhum.2023.1101938
Copyright (c) 2024 Bingke Wang, Hongkai Zhou
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
