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相关实验视频

Updated: Jul 15, 2025

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

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手动标记,二维姿势估计算法和基于3D标记的系统之间的步行分析比较.

Dimitrios Menychtas1, Nikolaos Petrou1, Ioannis Kansizoglou2

  • 1Biomechanics Laboratory, Department of Physical Education and Sports Science, Democritus University of Thrace, Komotini, Greece.

Frontiers in rehabilitation sciences
|September 25, 2023
PubMed
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人工智能姿势估计显示出步态分析的前景,但在小型关节运动方面存在困难. 一个允许纠正错误的AI工具可以使医疗专业人员能够获得先进的动力学测量.

科学领域:

  • 生物力学 生物力学
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 使用人工智能和计算机视觉 (CV) 的自动姿势估计为2D视频的动态测量提供了潜力,减少了对昂贵设备的依赖.
  • 尽管有进展,但人工智能姿势估计算法由于输出不稳定和难以纠正错误,因此尚未被卫生专业人士广泛采用.
  • 手动视频注释工具仍然很普遍,尽管效率和成本效益低于潜在的自动化解决方案.

研究的目的:

  • 为了评估AI的准确性,用于分析老年人群的步态周期的姿势估计算法 (OpenPose和Mediapipe).
  • 将AI的姿势估计与基于标记的3D运动捕捉系统 (Vicon) 和手动生物力学工具 (Kinovea) 进行比较.
  • 为了确定AI的特定局限性,在捕捉微妙的关节动力学方面提出估计.

主要方法:

  • 对使用分带跑步机的老年人进行步行周期分析.
  • 将OpenPose (OP) 和Mediapipe (MP) 的AI算法与Vicon 3D运动捕捉和Kinovea手动注释进行比较.
  • 使用布兰德-阿尔特曼图表和统计参数映射 (SPM) 进行统计分析以检测显著差异.

主要成果:

  • 人工智能姿势估计证明了可比的运动跟踪与总体步态的基于标记器的系统.
关键词:
2D姿势估计估计生物力学 生物力学生物力学视频注释联合角度比较 联合角度比较运动分析分析运动分析.

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Last Updated: Jul 15, 2025

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Published on: March 4, 2018

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  • 人工智能算法很难准确地识别具有小,关键运动的关节,例如脚.
  • 观察到小关节的解剖学标志的错误识别,与手动注释方法不同.

    • 结论:

    • 人工智能姿势估计显示了可访问的步态分析的潜力,但需要改进精确的小关节运动跟踪.
    • 手动工具虽然可靠地标识别,但引入了用户依赖的静态偏移.
    • 开发一个由人工智能驱动的注释工具,具有用户校正功能,可以弥合差距,为卫生专业人员提供具有成本效益和准确的动力学测量.