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无标记运动捕捉技术可在多种运动任务中准确预测地面反作用力。

Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks.

机构信息

School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia; School of Human Movement and Nutrition Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia.

School of Human Movement and Nutrition Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia.

出版信息

J Biomech. 2024 Mar;166:112051. doi: 10.1016/j.jbiomech.2024.112051. Epub 2024 Mar 15.

Abstract

Measuring or estimating the forces acting on the human body during movement is critical for determining the biomechanical aspects relating to injury, disease and healthy ageing. In this study we examined whether quantifying whole-body motion (segmental accelerations) using a commercial markerless motion capture system could accurately predict three-dimensional ground reaction force during a diverse range of human movements: walking, running, jumping and cutting. We synchronously recorded 3D ground reaction forces (force instrumented treadmill or in-ground plates) with high-resolution video from eight cameras that were spatially calibrated relative to a common coordinate system. We used a commercially available software to reconstruct whole body motion, along with a geometric skeletal model to calculate the acceleration of each segment and hence the whole-body centre of mass and ground reaction force across each movement task. The average root mean square difference (RMSD) across all three dimensions and all tasks was 0.75 N/kg, with the maximum average RMSD being 1.85 N/kg for running vertical force (7.89 % of maximum). There was very strong agreement between peak forces across tasks, with R values indicating that the markerless prediction algorithm was able to predict approximately 95-99 % of the variance in peak force across all axes and movements. The results were comparable to previous reports using whole-body marker-based approaches and hence this provides strong proof-of-principle evidence that markerless motion capture can be used to predict ground reaction forces and therefore potentially assess movement kinetics with limited requirements for participant preparation.

摘要

测量或估计人体在运动过程中所受的力对于确定与损伤、疾病和健康老龄化相关的生物力学方面至关重要。在这项研究中,我们研究了使用商用无标记运动捕捉系统量化全身运动(节段加速度)是否可以准确预测人体在多种运动(步行、跑步、跳跃和变向)中的三维地面反作用力。我们使用同步记录的 3D 地面反作用力(测力跑步机或地面测力板)和来自八个摄像机的高分辨率视频,这些摄像机相对于共同的坐标系进行了空间校准。我们使用商用软件来重建全身运动,同时使用几何骨骼模型来计算每个节段的加速度,从而计算整个身体的质心和地面反作用力在每个运动任务中的分布。所有三个维度和所有任务的平均均方根差(RMSD)为 0.75N/kg,最大平均 RMSD 为 1.85N/kg,用于跑步垂直力(最大力的 7.89%)。各任务之间的峰值力具有很强的一致性,R 值表明无标记预测算法能够预测所有轴和运动中约 95-99%的峰值力变化。结果与使用全身标记方法的先前报告相当,因此这为无标记运动捕捉可用于预测地面反作用力,从而可以在有限的参与者准备要求下评估运动动力学提供了有力的原理验证。

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