推进基于现场的垂直跳跃分析:无标记的姿势估计与力量板
Jelena Aleksic1, David Mesaroš2, Dmitry Kanevsky3
1Faculty of Sport and Physical Education, University of Belgrade, 11000 Belgrade, Serbia.
Life (Basel, Switzerland)
|January 8, 2025
概括
无标记器的2D姿势估计准确地分析了反运动垂直跳跃 (CMJ),为强力板提供了一个实用的替代方案. 这项技术使体育科学和康复的可扩展,数据驱动的评估成为可能.
科学领域:
- 生物力学和体育科学 生物力学和体育科学
- 人类运动分析 人类运动分析
- 康复技术 康复技术 康复技术
背景情况:
- 反运动垂直跳跃 (CMJ) 是运动和康复中下肢力量评估的关键指标.
- 传统的CMJ分析依赖于昂贵和实验室绑定的运动捕捉或力板系统.
- 需要为CMJ绩效评估提供可访问,现场部署的工具.
研究的目的:
- 为了验证MMPose的准确性,一个无标记的2D姿势估计框架,用于分析CMJ性能.
- 将从MMPose获得的CMJ指标与从黄金标准力板获得的CMJ指标进行比较.
主要方法:
- 12名健康参与者完成了5次CMJ试验,同时通过强力板和智能手机摄像头进行记录.
- 分析重点是垂直速度概况和时间变量 (跳跃时间,峰值高度,速度).
- 统计比较涉及布兰德-阿尔特曼分析,相关系数 (r) 和类内相关系数 (ICC).
主要成果:
- 在CMJ分析中,MMPose与力板有很强的一致性 (r = 0.992).
- 时间变量显示出高可靠性 (ICC>0.9) 与最小的系统差异.
- 对于大多数分析的变量,无标记系统的效果大小可以忽略不计.
结论:
- MMPose为CMJ分析提供了一种具有成本效益和实用的无标记器解决方案.
- 这种技术适用于基于现场的评估,在这种情况下,力板是不可访问的.
- 在体育表现和康复方面,MMPose促进了可扩展的,数据驱动的运动分析.
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