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Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

机译:肘关节的肌肉骨骼模型开发及实验评估

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摘要

A dynamic musculoskeletal model of the elbow joint in which muscle, ligament, and articular surface contact forces are predicted concurrently would be an ideal tool for patient-specific preoperative planning, computer-aided surgery, and rehabilitation. Existing musculoskeletal elbow joint models have limited clinical applicability because of idealizing the elbow as a mechanical hinge joint or ignoring important soft tissue (e.g., cartilage) contributions. The purpose of this study was to develop a subject-specific anatomically correct musculoskeletal elbow joint model and evaluate it based on experimental kinematics and muscle electromyography measurements. The model included three-dimensional bone geometries, a joint constrained by multiple ligament bundles, deformable contacts, and the natural oblique wrapping of ligaments. The musculoskeletal model predicted the bone kinematics reasonably accurately in three different velocity conditions. The model predicted timing and number of muscle excitations, and the normalized muscle forces were also in agreement with the experiment. The model was able to predict important in vivo parameters that are not possible to measure experimentally, such as muscle and ligament forces, and cartilage contact pressure. In addition, the developed musculoskeletal model was computationally efficient for body-level dynamic simulation. The maximum computation time was less than 30 min for our 35 s simulation. As a predictive clinical tool, the potential medical applications for this model and modeling approach are significant.
机译:同时预测肌肉,韧带和关节表面接触力的肘关节动态肌肉骨骼模型将是针对特定患者的术前计划,计算机辅助手术和康复的理想工具。现有的肌肉骨骼肘关节模型由于将肘部理想化为机械铰链关节或忽略了重要的软组织(例如软骨)的贡献而在临床上的应用受到限制。这项研究的目的是建立特定于受试者的解剖学正确的肌肉骨骼肘关节模型,并根据实验运动学和肌肉肌电图测量结果对其进行评估。该模型包括三维骨骼几何形状,受多个韧带束约束的关节,可变形的接触以及韧带的自然倾斜包裹。肌肉骨骼模型在三种不同的速度条件下合理准确地预测了骨骼运动学。该模型预测了肌肉刺激的时机和次数,标准化的肌肉力也与实验一致。该模型能够预测无法通过实验测量的重要体内参数,例如肌肉和韧带力以及软骨接触压力。此外,所开发的肌肉骨骼模型对于人体水平的动态仿真具有较高的计算效率。对于我们的35 s仿真,最大计算时间少于30分钟。作为一种预测性临床工具,此模型和建模方法的潜在医学应用意义重大。

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