Baldwin Mark A, Laz Peter J, Stowe Joshua Q, Rullkoetter Paul J
Computational Biomechanics Lab, University of Denver, Denver, CO, USA.
Comput Methods Biomech Biomed Engin. 2009 Dec;12(6):651-9. doi: 10.1080/10255840902822550.
Verified and efficient representations of knee ligamentous constraints are essential to forward-dynamic models for prediction of knee mechanics. The objectives of this study were to develop an efficient probabilistic representation of knee ligamentous constraint using the advanced mean value (AMV) probabilistic approach, and to compare the AMV representation with the gold standard Monte Carlo (MC) approach. Specifically, the effects of inherent uncertainty in ligament stiffness, reference strain and attachment site locations on joint constraint were assessed. An explicit finite element model of the knee was evaluated under a series of anterior-posterior (AP) and internal-external (IE) loading at full extension and 90 degrees flexion. Distributions of AP and IE laxity were predicted using experimentally-based levels of ligament parameter variability. Importance factors identified the critical properties affecting the predicted bounds, and agreed with reported ligament recruitment. The AMV method agreed closely with MC results with a four-fold reduction in computation time.
经证实且高效的膝关节韧带约束表示对于预测膝关节力学的正向动力学模型至关重要。本研究的目的是使用高级均值（AMV）概率方法开发一种高效的膝关节韧带约束概率表示，并将AMV表示与金标准蒙特卡罗（MC）方法进行比较。具体而言，评估了韧带刚度、参考应变和附着位点位置的固有不确定性对关节约束的影响。在一系列前后（AP）和内外（IE）加载下，对膝关节的显式有限元模型在完全伸展和90度屈曲时进行了评估。使用基于实验的韧带参数变异性水平预测AP和IE松弛度的分布。重要性因素确定了影响预测范围的关键特性，并与报道的韧带募集情况一致。AMV方法与MC结果密切吻合，计算时间减少了四倍。
