Dept. of Mechanical and Materials Engineering, The University of Denver, CO, USA; Dept. of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.
Dept. of Mechanical and Materials Engineering, The University of Denver, CO, USA.
J Biomech. 2019 Feb 14;84:94-102. doi: 10.1016/j.jbiomech.2018.12.020. Epub 2018 Dec 28.
Concurrent multiscale simulation strategies are required in computational biomechanics to study the interdependence between body scales. However, detailed finite element models rarely include muscle recruitment due to the computational burden of both the finite element method and the optimization strategies widely used to estimate muscle forces. The aim of this study was twofold: first, to develop a computationally efficient muscle force prediction strategy based on proportional-integral-derivative (PID) controllers to track gait and chair rise experimental joint motion with a finite element musculoskeletal model of the lower limb, including a deformable knee representation with 12 degrees of freedom; and, second, to demonstrate that the inclusion of joint-level deformability affects muscle force estimation by using two different knee models and comparing muscle forces between the two solutions. The PID control strategy tracked experimental hip, knee, and ankle flexion/extension with root mean square errors below 1°, and estimated muscle, contact and ligament forces in good agreement with previous results and electromyography signals. Differences up to 11% and 20% in the vasti and biceps femoris forces, respectively, were observed between the two knee models, which might be attributed to a combination of differing joint contact geometry, ligament behavior, joint kinematics, and muscle moment arms. The tracking strategy developed in this study addressed the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations and can be used with finite element musculoskeletal models to efficiently estimate the interdependence between muscle forces and tissue deformation.
在计算生物力学中，需要采用并发多尺度模拟策略来研究身体尺度之间的相互依赖性。然而，由于有限元方法和广泛用于估计肌肉力的优化策略的计算负担，详细的有限元模型很少包括肌肉募集。本研究的目的有两个：首先，开发一种基于比例-积分-微分（PID）控制器的计算效率高的肌肉力预测策略，以跟踪步态和椅子上升实验关节运动，使用包括具有 12 个自由度的可变形膝关节表示的下肢有限元骨骼肌肉模型；其次，演示关节水平的可变形性通过使用两种不同的膝关节模型来影响肌肉力估计，并比较两种解决方案之间的肌肉力。PID 控制策略以均方根误差低于 1°的精度跟踪了实验髋、膝和踝关节的屈伸运动，并与先前的结果和肌电图信号很好地估计了肌肉、接触和韧带力。在两种膝关节模型之间，股四头肌和股二头肌的力分别存在高达 11%和 20%的差异，这可能归因于关节接触几何形状、韧带行为、关节运动学和肌肉力臂的差异组合。本研究中开发的跟踪策略解决了骨骼肌肉模拟中计算成本和模型细节之间不可避免的权衡问题，并可与有限元骨骼肌肉模型一起用于高效估计肌肉力和组织变形之间的相互依赖性。
