Nagaraja Vikranth H, Bergmann Jeroen H M, Andersen Michael S, Thompson Mark S
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 3PJ, UK.
Department of Materials and Production, Aalborg University, Fibigerstraede 16, Aalborg East DK-9220, Denmark.
J Biomech Eng. 2023 Apr 1;145(4). doi: 10.1115/1.4056172.
Reliably and accurately estimating joint/segmental kinematics from optical motion capture data has remained challenging. Studies objectively characterizing human movement patterns have typically involved inverse kinematics and inverse dynamics techniques. Subsequent research has included scaled cadaver-based musculoskeletal (MSK) modeling for noninvasively estimating joint and muscle loads. As one of the ways to enhance confidence in the validity of MSK model predictions, the kinematics from the preceding step that drives such a model needs to be checked for agreement or compared with established/widely used models. This study rigorously compares the upper extremity (UE) joint kinematics calculated by the Dutch Shoulder Model implemented in the AnyBody Managed Model Repository (involving multibody kinematics optimization (MKO)) with those estimated by the Vicon Plug-in Gait model (involving single-body kinematics optimization (SKO)). Ten subjects performed three trials of (different types of) reaching tasks in a three-dimensional marker-based optical motion capture laboratory setting. Joint angles, processed marker trajectories, and reconstruction residuals corresponding to both models were compared. Scatter plots and Bland-Altman plots were used to assess the agreement between the two model outputs. Results showed the largest differences between the two models for shoulder, followed by elbow and wrist, with all root-mean-squared differences less than 10 deg (although this limit might be unacceptable for clinical use). Strong-to-excellent Spearman's rank correlation coefficients were found between the two model outputs. The Bland-Altman plots showed a good agreement between most of the outputs. In conclusion, results indicate that these two models with different kinematic algorithms broadly agree with each other, albeit with few key differences.
从光学运动捕捉数据中可靠且准确地估计关节/节段运动学仍然具有挑战性。客观描述人类运动模式的研究通常涉及逆运动学和逆动力学技术。后续研究包括基于尸体的缩放肌肉骨骼（MSK）建模，用于无创估计关节和肌肉负荷。作为增强对MSK模型预测有效性信心的方法之一，需要检查驱动此类模型的上一步运动学是否一致，或与已建立/广泛使用的模型进行比较。本研究严格比较了在AnyBody管理模型库中实现的荷兰肩部模型（涉及多体运动学优化（MKO））计算的上肢（UE）关节运动学与Vicon插件步态模型（涉及单体运动学优化（SKO））估计的关节运动学。10名受试者在基于三维标记的光学运动捕捉实验室环境中进行了三次（不同类型的）伸手任务试验。比较了与两个模型对应的关节角度、处理后的标记轨迹和重建残差。使用散点图和布兰德-奥特曼图来评估两个模型输出之间的一致性。结果表明，两个模型在肩部的差异最大，其次是肘部和腕部，所有均方根差异均小于10°（尽管这一限制对于临床应用可能不可接受）。在两个模型输出之间发现了强到极好的斯皮尔曼等级相关系数。布兰德-奥特曼图显示大多数输出之间具有良好的一致性。总之，结果表明这两种具有不同运动学算法的模型大体上相互一致，尽管存在一些关键差异。
