Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements

bioRxiv [Preprint]. 2023 Feb 22:2023.02.21.526496. doi: 10.1101/2023.02.21.526496.

Abstract

Motion analysis is essential for assessing in-vivo human biomechanics. Marker-based motion capture is the standard to analyze human motion, but the inherent inaccuracy and practical challenges limit its utility in large-scale and real-world applications. Markerless motion capture has shown promise to overcome these practical barriers. However, its fidelity in quantifying joint kinematics and kinetics has not been verified across multiple common human movements. In this study, we concurrently captured marker-based and markerless motion data on 10 healthy subjects performing 8 daily living and exercise movements. We calculated the correlation (R xy ) and root-mean-square difference (RMSD) between markerless and marker-based estimates of ankle dorsi-plantarflexion, knee flexion, and three-dimensional hip kinematics (angles) and kinetics (moments) during each movement. Estimates from markerless motion capture matched closely with marker-based in ankle and knee joint angles (R xy ≥ 0.877, RMSD ≤ 5.9°) and moments (R xy ≥ 0.934, RMSD ≤ 2.66 % height × weight). High outcome comparability means the practical benefits of markerless motion capture can simplify experiments and facilitate large-scale analyses. Hip angles and moments demonstrated more differences between the two systems (RMSD: 6.7° - 15.9° and up to 7.15 % height × weight), especially during rapid movements such as running. Markerless motion capture appears to improve the accuracy of hip-related measures, yet more research is needed for validation. We encourage the biomechanics community to continue verifying, validating, and establishing best practices for markerless motion capture, which holds exciting potential to advance collaborative biomechanical research and expand real-world assessments needed for clinical translation.

Publication types

  • Preprint