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足球运动员的力量训练方法使用基于机器学习的图像处理.

Xiaoxiang Cao1, Xiaodong Zhao2, Huan Tang3

  • 1School of Physical Education and Health, Hangzhou Normal University, Hangzhou, 310036, Zhejiang, China.

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概括

功能性力量训练显著改善了足球运动员的体能,包括,跑步和投. 机器学习通过分析玩家的运动来提高训练效率.

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科学领域:

  • 运动科学 运动科学 运动科学
  • 生物机械分析 生物机械分析
  • 运动中的机器学习

背景情况:

  • 运动员和普通人的体力状况下降是一个越来越令人担忧的问题.
  • 足球运动员需要特定的身体能力,这些能力可以通过有针对性的训练来提高.
  • 当前的训练方法可能无法完全优化表现或满足个体球员的需求.

研究的目的:

  • 评估功能力量训练对青少年足球运动员身体能力的影响.
  • 开发和应用机器学习模型来分析和改进动作.
  • 为了提高足球的整体训练效率.

主要方法:

  • 一项随机对照试验,包括116名青少年足球运动员 (8-13岁),分为实验组和对照组.
  • 实验组接受了15-20分钟的功能性力量训练,持续24个会议.
  • 使用反向传播神经网络 (BPNN) 分析基于运动速度,灵敏度和力量的动行为.

主要成果:

  • 与实验前水平相比,实验组在得分上显示了统计学上显著的改善.
  • 在穿跑,投和球方面,观察到两组之间存在显著差异.
  • 机器学习模型有效地分析了动作,以提高训练效率.

结论:

  • 功能性力量训练在提高足球运动员的力量和灵敏度方面非常有效.
  • 将功能力量训练纳入足球课程可以带来显著的性能改善.
  • 机器学习为客观运动分析和个性化训练优化提供了有价值的工具.