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首页> 外文期刊>Journal of Biomechanics >Inter-insertional distance is a poor correlate for ligament load: Analysis from in vivo gait kinetics data
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Inter-insertional distance is a poor correlate for ligament load: Analysis from in vivo gait kinetics data

机译：插入距离与韧带负荷之间的关系不大：从体内步态动力学数据分析

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In many analytic models of the knee joint, inter-insertional distance is used as the measure to define the load in a ligament. In addition, the direction of the load is taken to be the direction between the two insertions. Our in vivo data on the ovine ligament loads during gait, however, indicate that a wide range of forces is possible in the ligament for any specified inter-insertional distance. To understand the complex relationship between the bone orientations and ligament load better, an artificial neural network (ANN) model was developed. The six degree-of-freedom (6-DOF) in vivo kinematics of femur relative to tibia (joint kinematics) was used as input, and the magnitude of the anterior cruciate ligament (ACL) load was used as output/target. While the trained network was able to predict peak ligament loads with remarkable accuracy (R-square=0.98), an explicit relationship between joint kinematics and ACL load could not be determined. To examine the experimental and ANN observations further, a finite element (FE) model of the ACL was created. The geometry of the FE model was reconstructed from magnetic resonance images (MRI) of an ACL, and an isotropic, hyperelastic, nearly incompressible constitutive model was implemented for the ACL. The FE simulation results also indicate that a range of loads is possible in the ACL for a given inter-insertional distance, in concordance with the experimental/ANN observations. This study provides new insights for models of the knee joint; a simple force-length relationship for the ligament is not exact, nor is a single point to single point direction. More detailed microstructure-function data is required.
机译：在许多膝关节分析模型中，插入间距离用作定义韧带负荷的量度。另外，载荷的方向被认为是两次插入之间的方向。然而，我们关于步态中绵羊韧带负荷的体内数据表明，对于任何指定的插入间距离，韧带中可能存在很大范围的作用力。为了更好地了解骨骼取向与韧带负荷之间的复杂关系，开发了人工神经网络（ANN）模型。股骨相对于胫骨的六自由度（6-DOF）体内运动学（关节运动学）用作输入，前交叉韧带（ACL）的大小用作输出/目标。虽然训练有素的网络能够以极高的准确性预测韧带峰值负荷（R平方= 0.98），但无法确定关节运动学与ACL负荷之间的明确关系。为了进一步检查实验和人工神经网络的观察结果，创建了ACL的有限元（FE）模型。从ACL的磁共振图像（MRI）重建了FE模型的几何，并为ACL实现了各向同性，超弹性，几乎不可压缩的本构模型。有限元仿真结果还表明，与实验/ ANN观测结果一致，对于给定的插入距离，ACL中可能存在一定范围的载荷。这项研究为膝关节模型提供了新的见解。韧带的简单力长关系不精确，单点到单点方向也不精确。需要更详细的微结构功能数据。

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