通过核心力量训练最大限度地提高羽毛球运动员的表现:利用机器学习 (ML) 建模释放他们的全部潜力
Shuzhen Ma1,2, Kim Geok Soh1, Salimah Binti Japar1
1Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Selangor, 43400, Malaysia.
有针对性的核心力量训练通过提高稳定性,敏捷性和力量来显著提高羽毛球表现. 集成数据驱动的方法,如人工神经网络 (ANN),优化个性化训练,以达到顶峰的运动能力和预防伤害.
科学领域:
- 体育科学 运动科学 运动科学
- 生物力学 生物力学
- 数据科学在体育中的应用
背景情况:
- 核心力量对于羽毛球表现至关重要,影响稳定性,功率转移和敏捷性.
- 优化核心训练需要了解其对特定运动技能 (如服务和粉碎) 的特定影响.
- 现有的研究研究了球拍运动的核心训练,突出了其潜在的好处.
研究的目的:
- 研究核心运动对羽毛球运动员稳定性,敏捷性和力量的影响.
- 探索羽毛球的生物力学要求和核心肌肉的作用.
- 将机器学习应用于个性化的核心培训计划开发.
主要方法:
- 关于羽毛球生物力学和核心训练的综合文献综述.
- 对有关球拍运动的核心训练研究的分析.
- 使用运动员数据开发和验证人工神经网络 (ANN).
主要成果:
- 有针对性的核心炼显著提高了羽毛球运动员的稳定性,敏捷性和力量.
- 核心训练提高了性能,同时降低了受伤风险.
- 该ANN准确地预测了优化核心强化程序的性能提升.
结论:
- 数据驱动的,个性化的核心力量训练对于优化羽毛球运动员能力至关重要.
- 人工神经网络在分析复杂的运动员数据以进行个性化训练方面表现有前途.
- 将定制的核心训练整合到计划中可以提高表现,并最大限度地减少受伤的可能性.
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