Are subject-specific musculoskeletal models robust to parameter identification?

This study analyzed the sensitivity of the predictions of an MRI-based musculoskeletal model (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the unavoidable uncertainties in parameter identification, i.e., body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=978#pack_1643">1. Software </a> : This package contains the software developed (NMSBuilder and PMM) and used in the present paper. NMSBuilder allows biomedical data processing and creation of subject-specific musculoskeletal models for OpenSim. It includes a graphical user interface that integrates the functionalities of the Multimode Application Framework and the OpenSim API. The Probabilistic Musculoskeletal Modeling module interfaces MATLAB and the OpenSim API to access, visualize and modify model variables, and repeatedly run simulations of movement. </li> <li> <a href="https://simtk.org/frs?group_id=978#pack_1642">2. Data </a> : This package contains MRI (DICOM), gait data (trc and mot files) and the sampled input variables (MAT-file) used in the study to create the image-based musculoskeletal model and run probabilistic simulations of gait. </li> <li> <a href="https://simtk.org/frs?group_id=978#pack_1641">3. Results </a> : This package contains post-processed results (MAT-files) of the probabilistic simulations of gait </li> </ul>

本研究旨在探讨基于 MRI 的肌肉骨骼模型（即关节角度、关节力矩、肌肉和关节接触力）在行走过程中的预测敏感性，针对参数识别中不可避免的误差，例如身体标志点位置、最大肌肉张力和肌腱几何形状。为此，我们构建了一个基于 MRI 的下肢肌肉骨骼模型，该模型被定义为包含 7 个节段、10 个自由度的关节链，并由 84 个肌腱单元驱动。随后，我们根据参数的不确定性进行了蒙特卡洛概率分析，利用 500 个模型解决典型的逆动力学和静态优化问题，这些模型包含了不同组别的扰动变量值。模型构建和步态模拟均通过我们开发的免费软件进行，该软件旨在标准化模型构建过程，与 OpenSim 集成，并创建运动的概率模拟。本项研究涉及以下软件/数据包：

<ul>
<li>1. 软件：该软件包包含本论文中开发并使用的软件（NMSBuilder 和 PMM）。NMSBuilder 允许生物医学数据处理并创建针对 OpenSim 的特定个体肌肉骨骼模型。它包括一个图形用户界面，该界面集成了多模式应用框架和 OpenSim API 的功能。概率肌肉骨骼建模模块将 MATLAB 与 OpenSim API 集成，以便访问、可视化和修改模型变量，并反复运行运动模拟。</li>
<li>2. 数据：该数据包包含 MRI（DICOM）、步态数据（trc 和 mot 文件）以及用于创建基于图像的肌肉骨骼模型和进行步态概率模拟的采样输入变量（MAT 文件）。</li>
<li>3. 结果：该数据包包含步态概率模拟的后处理结果（MAT 文件）。</li>
</ul>