Problem
Research questions and friction points this paper is trying to address.
markerless body tracking
biomechanical analysis
running
joint angles
human pose estimation
VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running
🤖 AI Summary
研究通过使用不同人体姿态估计器分析跑步时的关键关节角度，利用深度学习模型和后处理模块减少误差，提高生物力学分析精度。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Markerless Body Tracking
Biomechanical Analysis
Human Pose Estimation
Post-processing Module
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