发表机构
BiometricsAI, Universidad Autonoma de Madrid (UAM); Biomechanics Lab, Universidad Politecnica de Madrid (UPM)（生物识别AI中心，马德里自治大学（UAM）; 生物力学实验室，马德里理工大学（UPM））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发VideoRun2D演示，采用人体姿态跟踪器结合专家标注，分析44名跑者314次短跑的髋、膝关键关节角度，经后处理模块优化后误差降低，证明人体姿态跟踪可用于跑步生物力学分析。
AI 中文摘要
由于深度学习模型的发展、数据可用性的提升以及计算资源的改善，人体姿态估计已取得显著进展，这些进展催生了高精度的人体跟踪系统，可直接应用于运动分析与性能评估。VideoRun2D演示采用不同的人体姿态估计器对短跑过程进行生物力学分析，所提出的框架通过人体姿态跟踪器与专家手动标注进行评估，测试时使用了44名专业跑者的314次短跑数据，重点关注短跑生物力学中的两个关键关节角度：1）髋部屈伸；2）膝部屈伸。该框架还包含一个用于异常值检测的后处理模块，测试结果显示，最佳跟踪器的平均均方根误差范围为11.46°至5.83°，与后处理模块集成后，这些误差可分别降至9.87°和5.30°。VideoRun2D演示的研究结果表明，人体姿态跟踪方法可成为跑步生物力学分析的宝贵资源。
英文摘要
Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments have led to highly accurate body tracking systems with direct applications in sports analysis and performance evaluation. The VideoRun2D Demo performs a biomechanical analysis during sprints using different human pose estimators. The proposed framework was evaluated using human pose trackers and expert manual annotations. The tested framework uses 314 sprints from 44 professional runners, focusing on two key joint angles in sprint biomechanics: 1) hip flexion/extension and 2) knee flexion/extension. The framework also includes a post-processing module for outlier detection. The tested results demonstrate that the average root-mean-square errors range from 11.46° to 5.83° for the best trackers. When integrated with the post-processing modules, these errors can be reduced to 9.87° and 5.30°, respectively. The VideoRun2D Demo findings suggest that human pose-tracking approaches can be valuable resources for the biomechanical analysis of running.
Comments5 pages, 4 figures, 2 tables. IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2026 (1st PhysHuman Workshop: Physically Grounded Human Perception and Modeling)
