Maximizing the performance of badminton athletes through core strength training: Unlocking their full potential using machine learning (ML) modeling

被引:1
作者
Ma, Shuzhen [1 ,2 ]
Soh, Kim Geok [1 ]
Japar, Salimah Binti [1 ]
Xu, Simao [3 ]
Guo, Zhicheng [4 ]
机构
[1] Univ Putra Malaysia, Fac Educ Studies, Dept Sports Studies, Serdang 43400, Malaysia
[2] Guilin Univ Technol, Coll Publ Adm, Guilin 541004, Peoples R China
[3] Chengdu Sport Univ, Chengdu 61004, Peoples R China
[4] Guangxi Arts & Crafts Sch, Liuzhou 545005, Peoples R China
关键词
Badminton performance; Core strength training; Athlete potential; Stability and agility; Injury prevention; Artificial neural network;
D O I
10.1016/j.heliyon.2024.e35145
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Core strength training plays an essential role in maximizing performance for badminton athletes. The core muscles in the abdominal, back, and hip regions provide stability, enable efficient power transfer between the upper and lower body, and allow for rapid changes in direction - all crucial components for success in badminton. However, optimizing core training requires an understanding of its impact on sport-specific skills. A variety of exercises targeting the abdominal, back, and hip muscles are discussed. Incorporating core strength training into regular regimens can improve athletes' overall strength, endurance, balance, control, and prevent injuries. This study investigates the effects of various core exercises on stability, agility, and power in badminton players. A comprehensive literature review was conducted to explore the biomechanical demands of badminton and how core musculature contributes to movements like serving, smashing, and lunging. Studies assessing the effects of core training programs in related racquet sports were also examined. The results indicate that targeted core exercises significantly improve athletes' stability, agility, and power output. Exercises targeting the abdominal, back, and hip muscles enhance performance capabilities while reducing injury risk. Machine learning (ML) techniques are then applied to further analyze the relationship between core training and athletic performance. An Artificial Neural Network (ANN) is developed using a dataset of athletes' training histories, metrics, and injury profiles. The model predicts enhancements to stability, agility, and strength from optimized core strengthening routines. Validation confirms the network accurately captures the complex interactions between training variables and physical attributes. This integrated approach provides evidence-based guidelines for tailoring individualized training regimens to unleash players' full abilities. ANNs hold promise for analyzing large datasets on athletes' performance metrics, training variables, and injury histories to design personalized training programs. Linear regression analysis confirmed the ANN's accurate predictions. The findings emphasize integrating data-driven core strength training tailored for badminton into comprehensive programs can help optimize physical abilities and elevate performance levels.
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页数:14
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