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基于OpenPose的运动分析在垂直跳跃测试中测量膝盖的有效性和可靠性
Takumi Ino1,2, Mina Samukawa3, Tomoya Ishida3
1Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan.
Journal of sports science & medicine
|September 4, 2024
概括
基于OpenPose的运动分析 (OpenPose-MA) 为评估膝盖角的传统方法提供了一种经济高效的替代方案. 这种深度学习技术的准确性和可复制性与人类分析相比,使其成为运动分析的可行工具.
科学领域:
- 生物力学 生物力学
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 传统的3D运动分析 (3D-MA) 和基于人体视觉检测的运动分析 (Human-MA) 用于人体运动估计受到成本,时间和实验约束的限制.
- 基于OpenPose的运动分析 (OpenPose-MA) 利用深度学习来进行二维姿势估计,提供一种潜在的更容易获得的方法.
研究的目的:
- 评估OpenPose-MA与Human-MA之间的精度,使用3D-MA作为黄金标准.
- 为了比较OpenPose-MA和Human-MA之间的膝盖角测量的可重现性,准确性和波形相似性.
主要方法:
- 一组由21名健康成年人组成的队列执行了垂直跳跳任务.
- 用OpenPose-MA (2D姿势估计) 和Human-MA (理疗师评估) 测量了膝盖角.
- 三维运动分析 (3D-MA) 作为比较的参考标准.
主要成果:
- 开放式MA和人体MA显示出与3DMA相比的出色可复制性 (ICC).
- 在OpenPose-MA和Human-MA之间,对于膝盖角的平均绝对误差 (MAE) 或多重相关系数 (CMC) 没有发现显著差异.
- 皮尔森的相关系数表明,OpenPose-MA (0.97) 和Human-MA (0.98) 与3D-MA之间有很强的一致性.
结论:
- OpenPose-MA为膝盖角评估提供了令人满意的可重现性和准确性,与Human-MA.
- 基于深度学习的OpenPose-MA表现出波形类似于3D-MA,类似于人类-MA.
- OpenPose-MA是一种可行,准确和可重复的方法,用于分析膝盖在动态运动时,如垂直跳跃.
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