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The VideoRun2D Demo leverages advanced human pose estimation techniques to conduct a biomechanical analysis of sprinting, utilizing data from 314 sprints performed by 44 professional runners. By focusing on critical joint angles鈥攈ip and knee flexion/extension鈥攁nd employing a post-processing module for outlier detection, the framework achieves significant accuracy improvements, with root-mean-square errors reduced to as low as 5.30 degrees. These results underscore the potential of markerless body tracking systems in enhancing sports performance evaluation and biomechanics research.
Markerless body tracking can achieve remarkable accuracy in biomechanical analysis, with errors as low as 5.30 degrees in sprinting evaluations.
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{\deg} to 5.83{\deg} for the best trackers. When integrated with the post-processing modules, these errors can be reduced to 9.87{\deg} and 5.30{\deg}, respectively. The VideoRun2D Demo findings suggest that human pose-tracking approaches can be valuable resources for the biomechanical analysis of running.
