用无标记动作捕捉分析临床运动数据，效果接近传统设备。
Validation of Human Pose Estimation and Human Mesh Recovery for Extracting Clinically Relevant Motion Data from Videos
- 对比无标记姿态估计与惯性单元、光学标记系统
- 无标记方法在临床小速度动作中误差可接受
- 部署快、操作简单，适合临床场景
本研究探讨了运动学分析工具的现状，涵盖体育生物力学中的先进手段如惯性测量单元（IMUs）和基于反光标记的光学动作捕捉（MoCap），以及计算领域新兴的无标记人体姿态估计与人体网格恢复技术。重点在于通过对比分析验证无标记动作捕捉在临床环境中的适用性，结果表明：相较于更复杂且便携性差的先进设备，无标记技术在运动学分析上的表现处于合理范围内。不仅人体姿态估计生成的结果与IMU和MoCap一致，还显著缩短了准备时间，并降低了设置所需的专业知识和技能门槛。尽管数据质量仍有提升空间，但该折衷在临床小速度测试中属于可接受的误差范围。
原文摘要 · Abstract (English)
This work aims to discuss the current landscape of kinematic analysis tools, ranging from the state-of-the-art in sports biomechanics such as inertial measurement units (IMUs) and retroreflective marker-based optical motion capture (MoCap) to more novel approaches from the field of computing such as human pose estimation and human mesh recovery. Primarily, this comparative analysis aims to validate the use of marker-less MoCap techniques in a clinical setting by showing that these marker-less techniques are within a reasonable range for kinematics analysis compared to the more cumbersome and less portable state-of-the-art tools. Not only does marker-less motion capture using human pose estimation produce results in-line with the results of both the IMU and MoCap kinematics but also benefits from a reduced set-up time and reduced practical knowledge and expertise to set up. Overall, while there is still room for improvement when it comes to the quality of the data produced, we believe that this compromise is within the room of error that these low-speed actions that are used in small clinical tests.
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