Quantifying uncertainty in inverse analyses from marker-based motion capture

Estimating kinematics from optical motion capture with skin-mounted markers, referred to as an inverse kinematic (IK) calculation, is the most common experimental technique in human motion analysis. Kinematics are often used to diagnose movement disorders and plan treatment strategies. In many such applications, small differences in joint angles can be clinically significant. Kinematics are also used to estimate joint powers, muscle forces, and other quantities of interest that cannot typically be measured directly. Thus, the accuracy and reproducibility of IK calculations are critical. In this work, we isolate and quantify the uncertainty in joint angles, moments, and powers due to two sources of error during IK analyses: errors in the placement of markers on the model (marker registration) and errors in the dimensions of the model's body segments (model scaling). We demonstrate that IK solutions are best presented as a distribution of equally probable trajectories when these sources of modeling uncertainty are considered. Notably, a substantial amount of uncertainty exists in the computed kinematics and kinetics even if low marker tracking errors are achieved. For example, considering only 2 cm of marker registration uncertainty, peak ankle plantarflexion angle varied by 15.9°, peak ankle plantarflexion moment varied by 26.6 N·m, and peak ankle power at push off varied by 75.9 W during healthy gait. This uncertainty can directly impact the classification of patient movements and the evaluation of training or device effectiveness, such as calculations of push-off power. We provide scripts in OpenSim so that others can reproduce our results and quantify the effect of modeling uncertainty in their own studies.Please cite the following publication:Uchida TK*, Seth A*. Conclusion or Illusion: Quantifying uncertainty in inverse analyses from marker-based motion capture due to errors in marker registration and model scaling. Frontiers in Bioengineering and Biotechnology 10: 874725, 2022 (*co-first authors). https://doi.org/10.3389/fbioe.2022.874725 <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=2302#pack_2329">Scripts </a> : Scripts for quantifying uncertainty in inverse analyses from marker-based motion capture due to errors in marker registration and model scaling. Includes example input and output files. </li> </ul>

通过皮肤贴片标记的光学动作捕捉进行运动学参数的估计，通常称为逆运动学（IK）计算，是人类运动分析中最常见的实验技术。运动学参数常被用于诊断运动障碍和制定治疗方案。在众多此类应用中，关节角度的微小差异在临床上可能具有显著意义。运动学参数亦用于估算关节力矩、肌肉力量以及其他难以直接测量的相关量。因此，逆运动学计算的准确性和可重复性至关重要。在本研究中，我们分离并量化了逆运动学分析过程中由于两种误差源引起的关节角度、力矩和功率的不确定性：模型上标记放置的误差（标记注册）和模型身体段尺寸的误差（模型缩放）。我们证明了，在考虑这些建模不确定性的情况下，逆运动学解的最佳呈现方式是等概率轨迹的分布。值得注意的是，即使在实现低标记跟踪误差的情况下，计算出的运动学和动力学也存在大量的不确定性。例如，仅考虑2厘米的标记注册不确定性，健康步态中峰值踝关节背屈角度变化了15.9°，峰值踝关节背屈力矩变化了26.6 N·m，峰值推离时的踝关节功率变化了75.9 W。这种不确定性会直接影响到患者运动的分类以及对训练或设备有效性的评估，例如推离功率的计算。我们提供了OpenSim中的脚本，以便他人能够重现我们的结果并量化他们自己研究中建模不确定性的影响。请引用以下出版物：Uchida TK*，Seth A*。结论或幻觉：基于标记动作捕捉的逆分析中，由于标记注册和模型缩放误差引起的运动学不确定性的量化。生物工程与生物技术前沿 10：874725，2022（*共同第一作者）。https://doi.org/10.3389/fbioe.2022.874725。本项目中包含以下软件/数据包：

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<a href="https://simtk.org/frs?group_id=2302#pack_2329">脚本 </a>：用于量化基于标记动作捕捉的逆分析中，由于标记注册和模型缩放误差引起的运动学不确定性的脚本。包括示例输入和输出文件。
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