Song Ke, Hullfish Todd J, Silva Rodrigo Scattone, Silbernagel Karin Grävare, Baxter Josh R
Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA.
Department of Physical Therapy, University of Delaware, Newark, DE, USA.
bioRxiv. 2023 Feb 22:2023.02.21.526496. doi: 10.1101/2023.02.21.526496.
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 ) 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 ≥ 0.877, RMSD ≤ 5.9°) and moments (R ≥ 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.
运动分析对于评估人体活体生物力学至关重要。基于标记物的运动捕捉是分析人体运动的标准方法，但固有的不准确性和实际挑战限制了其在大规模和现实世界应用中的效用。无标记运动捕捉已显示出克服这些实际障碍的潜力。然而，其在量化多个常见人体运动的关节运动学和动力学方面的保真度尚未得到验证。在本研究中，我们同时采集了10名健康受试者在进行8种日常生活和锻炼动作时基于标记物和无标记的运动数据。我们计算了无标记和基于标记物的踝关节背屈-跖屈、膝关节屈曲以及每个动作期间三维髋关节运动学（角度）和动力学（力矩）估计值之间的相关性（R）和均方根差（RMSD）。无标记运动捕捉的估计值与基于标记物的踝关节和膝关节角度（R≥0.877，RMSD≤5.9°）和力矩（R≥0.934，RMSD≤2.66%身高×体重）密切匹配。高结果可比性意味着无标记运动捕捉的实际益处可以简化实验并便于大规模分析。髋关节角度和力矩在两个系统之间表现出更多差异（RMSD：6.7° - 15.9°，高达7.15%身高×体重），尤其是在跑步等快速运动期间。无标记运动捕捉似乎提高了与髋关节相关测量的准确性，但仍需要更多研究进行验证。我们鼓励生物力学界继续验证、确认并建立无标记运动捕捉的最佳实践，这具有推进协作生物力学研究和扩大临床转化所需的现实世界评估的令人兴奋的潜力。
