COMPUTATIONAL FRAMEWORK FOR SIMULATING JOINT MECHANICS

机械载荷，特别是动态载荷，被认为在退行性关节疾病中起主要作用。此外，运动(即运动学)和载荷(即接触应力)经常相互作用以影响疾病的进展。因此，了解功能活动期间体内关节的运动和负荷将有助于解决这一临床上重要的问题。虽然动态成像技术的进步现在可以精确测量体内关节运动学，但目前还没有一种非侵入性的实验方法来测量体内关节载荷。因此，计算机模拟被用来制定预测，给出了对作用在关节上的肌肉力量的估计。然而，目前的刚体和变形建模方法无法计算膝关节等关键关节在运动过程中的接触应力结果。解决这一问题的合理方案是将可变形关节模型合并到更大的刚体动力学模型中，从而获得这两种方法的优点。然而，这种混合方法的计算成本目前是一个限制因素。因此，该项目建议开发一个用于研究人类关节力学的并行处理框架。该项目的具体目标如下：(1)建立可变形膝关节接触的动态肌肉骨骼模型。由于膝部是最常见的受伤关节，因此将首先研究膝部的可变形接触。(2)将该模型融入到并行处理优化框架中。并行处理将用于将预测优化的计算时间从几周减少到几小时。(3)评价模型对实验运动数据的预测能力。预先存在的运动实验数据将被用来评估模型预测运动和最终关节接触应力的能力。由此产生的功能性虚拟人体模型随后可以用于基础研究和临床应用。

Mechanical loading and especially dynamic loading, is believed to play a major role in degenerative joint diseases. Furthermore, motion (i.e., kinematics) and loading (i.e., contact stresses) often interact to influence disease progression. Thus, knowledge of in vivo joint motion and loading during functional activities would help address this clinically significant issue. While dynamic imaging advances now permit accurate measurement of in vivo joint kinematics, a non-invasive experimental approach does not exist for measuring in vivo joint loading. Consequently, computer simulations have been used to develop predictions given estimates of the muscle forces acting on the joint. However, current rigid body and deformable modeling approaches are not able to calculate contact stress results during movement in critical joints such as the knee. A logical solution to this problem is to incorporate deformable joint models into a larger rigid body dynamic model, thereby obtaining the advantages of both approaches. However, the computation cost of such a hybrid approach is currently a limiting factor. This project therefore proposes the development of a parallel-processing framework for studying human joint mechanics. The specific aims of the project are as follows: (1) Create a dynamic musculoskeletal model with deformable knee joint contact. Deformable contact in the knee will be studied initially since the knee is the most commonly injured joint. (2) Incorporate this model into a parallel-processing optimization framework. Parallel processing will be used to reduce the computational time for predictive optimizations from weeks to a matters of hours. (3) Evaluate the model's ability to predict experimental movement data. Pre-existing experimental movement data will be used to evaluate the model's ability to predict motion and ultimately joint contact stresses. The resulting functional virtual human model can then be used for basic research and clinical applications.