自动选择功能,以执行跳转轮体操的第二单元
Eiji Kitajima1, Takashi Sato2, Koji Kurata3
1Graduate School of Engineering and Science, University of the Ryukyus, Nakagami, Okinawa, Japan.
PloS one
|June 23, 2023
概括
本研究介绍了一种机器学习框架,用于分析顶扣除. 关键的发现表明,在皮克骑行期间,车轮上的时间和膝盖的角度对于尽量减少轮体操中执行得分的扣除至关重要.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 机器学习 机器学习
背景情况:
- 体操中的跳跃活动涉及复杂的运动,需要精确执行.
- 执行分数 (E-score) 的扣除对于评估绩效至关重要.
- 客观地分析运动特征可以改善训练和判断.
研究的目的:
- 开发一种机器学习框架,用于分析在顶安装阶段的运动特征和E-score扣除之间的关系.
- 为了识别影响体事件2单元期间扣除的关键移动特征.
主要方法:
- 开发了一个使用循环全对场变换 (RAFT) 和XMem进行视频分析的自动化系统.
- 使用OpenPose进行无标记的姿势估计,以提取关节坐标和角度.
- 构建了一个随机森林回归模型,以估计基于联合角度的E-score扣除.
主要成果:
- 实现了E-score扣除的精确估计,确定系数为0.79.
- 确定了"车轮上的时间"和"在皮克山上的膝盖角度"作为避免扣除的关键特征.
- 选择的特点与法官的观察和顶法规保持一致.
结论:
- 拟议的框架为分析体操表现提供了一个客观的方法.
- 专注于轮子和膝盖角度的时间可以帮助体操运动员改善他们的跳跃执行.
- 这种方法支持技能发展和遵守竞争规则.
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