VideoRun2D: Cost-Effective Markerless Motion Capture for Sprint Biomechanics

TL;DR

VideoRun2D is a cost-effective markerless motion capture system adapted for sprint biomechanics, demonstrating promising accuracy in key angle estimation but requiring further refinement for high-demand applications.

Contribution

This work adapts and evaluates general machine learning-based trackers for realistic sprint biomechanics analysis, introducing VideoRun2D as a specialized tool.

Findings

MoveNet accurately estimates key sprint angles with errors of 3.2° to 5.5°

CoTracker showed significant deviations from manual tracking

VideoRun2D demonstrates potential for sprint kinematic evaluation

Abstract

Sprinting is a determinant ability, especially in team sports. The kinematics of the sprint have been studied in the past using different methods specially developed considering human biomechanics and, among those methods, markerless systems stand out as very cost-effective. On the other hand, we have now multiple general methods for pixel and body tracking based on recent machine learning breakthroughs with excellent performance in body tracking, but these excellent trackers do not generally consider realistic human biomechanics. This investigation first adapts two of these general trackers (MoveNet and CoTracker) for realistic biomechanical analysis and then evaluate them in comparison to manual tracking (with key points manually marked using the software Kinovea). Our best resulting markerless body tracker particularly adapted for sprint biomechanics is termed VideoRun2D. The experimental development and assessment of VideoRun2D is reported on forty sprints recorded with a video camera from 5 different subjects, focusing our analysis in 3 key angles in sprint biomechanics: inclination of the trunk, flex extension of the hip and the knee. The CoTracker method showed huge differences compared to the manual labeling approach. However, the angle curves were correctly estimated by the MoveNet method, finding errors between 3.2{\deg} and 5.5{\deg}. In conclusion, our proposed VideoRun2D based on MoveNet core seems to be a helpful tool for evaluating sprint kinematics in some scenarios. On the other hand, the observed precision of this first version of VideoRun2D as a markerless sprint analysis system may not be yet enough for highly demanding applications. Future research lines towards that purpose are also discussed at the end: better tracking post-processing and user- and time-dependent adaptation.