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通过单一摄像头和计算机视觉进行无标记步态分析.

Hanwen Wang1, Bingyi Su1, Lu Lu1

  • 1Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA.

Journal of biomechanics
|March 2, 2024
PubMed
概括
此摘要是机器生成的。

使用计算机视觉的无标记步行分析显示出对健康的评估具有前景,在大多数参数中与基于标记器的系统相对相关. 然而,用于临床使用的准确性需要改进,特别是在相机角度和距离方面.

关键词:
深度神经网络是一种深度神经网络.步行参数 步行参数关节运动学 关节运动学下肢的下肢是什么意思运动追踪器是什么?

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科学领域:

  • 生物力学 生物力学
  • 计算机视觉 计算机视觉
  • 人类运动分析分析

背景情况:

  • 定量步态分析提供了健康见解.
  • 无标记的人体姿势估计提供了从视频中提取步态参数的潜力.

研究的目的:

  • 对比下肢动力学和时空参数的无标记和基于标记的步态分析.
  • 评估相机视角和距离对无标记方法准确性的影响.

主要方法:

  • 单摄像头无标记的人体姿势估计.
  • 基于标记器的运动跟踪系统.
  • 在健康的人口中进行对比.

主要成果:

  • 对于大多数时空步行参数,无标记和基于标记的方法之间存在强烈的相关性 (Rxy > 0.75).
  • 对于部和膝关节关节动力学的高相关性 (Rxy > 0.8).
  • 摄像机视角和距离对准确度的显著影响.

结论:

  • 无标记步行分析是一般应用程序的可行替代方案,在这些应用程序中,标记器是不切实际的.
  • 目前的无标记方法的准确性不足以进行临床诊断,需要进一步开发.
  • 未来的研究应该专注于提高准确性和探索病态步行分析中的应用.