量化MediaPipe与已知的标准之间的相似性,以解决跟踪2D上肢轨迹的问题:概念验证研究
Vaidehi Wagh1, Matthew W Scott1,2, Sarah N Kraeutner1
1Neuroplasticity, Imagery, and Motor Behaviour Lab, Department of Psychology, University of British Columbia, Kelowna, BC, Canada.
JMIR formative research
|December 19, 2024
概括
像MediaPipe一样,使用AI的无标记运动跟踪显示出临床使用的希望,但需要进一步验证. 这项研究发现MediaPipe.
科学领域:
- 人与计算机的交互
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
背景情况:
- 无标记运动跟踪为临床环境中基于标记器的系统提供了一个可行的替代方案.
- 人工智能 (AI) 能够实现轻量级,无标记的动作捕捉.
- 像MediaPipe这样的AI系统对上肢和手部的精细运动的准确性需要调查.
研究的目的:
- 为了评估MediaPipe的2D精度,用于跟踪上肢的细微运动.
- 将MediaPipe的性能与经过验证的触摸屏标准进行比较.
主要方法:
- 十名参与者在触摸屏上以各种速度跟踪光标轨迹.
- 触摸屏和视频摄像头同时捕捉运动数据.
- 来自触摸屏和MediaPipe的2D坐标使用转换后的根平均平方误差 (RMSE) 进行了比较.
主要成果:
- 媒体管显示了与触摸屏的形状相似性,转换改善了重叠.
- 平均RMSE为0.28个正常化像素,表明统计学上显著但差异很小.
- 精度在30和60FPS之间是一致的,但在120FPS下降.
结论:
- 基于AI的动作捕捉,例如MediaPipe,可以以可量化的准确度跟踪细微的上肢运动.
- 这些发现支持MediaPipe在临床和研究环境中的潜在应用.
- 未来的研究应该专注于3D精度,以充分验证移动捕捉的AI系统.
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