Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis
Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis
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DOI：
10.1115/1.4032379
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发表时间：
2016-03-01
影响因子：
1.7
通讯作者：
Shelburne, Kevin B.
中科院分区：
文献类型：
--
作者：
Navacchia, Alessandro;Myers, Casey A.;Shelburne, Kevin B.
肌肉骨骼模型是一种强大的工具，可以进行生物力学研究和预测肌肉力量，而这是实验无法实现的。建模者必须面对的一个核心挑战是验证。肌肉活动和关节负荷的测量用于肌肉力量预测的定性和间接验证。特定于主题的模型已经达到了高度的复杂性，并可以预测接触负荷惊人的准确性。然而，每一个确定性的肌肉骨骼模型包含一个内在的不确定性，由于大量的参数在体内无法识别。这项工作的目的是测试内在的不确定性的影响，在一个规模通用模型的肌肉和关节负荷的估计。使用全局概率方法（单个分析中包含多个不确定性）对标记放置、肢体冠状面对齐、身体节段参数、Hill型肌肉参数和肌肉几何形状进行建模。对于来自“大挑战赛”的3名植入遥测膝关节植入物的受试者的步态活动，估计了预测膝关节压缩载荷和内翻/外翻接触力矩的5-95%置信区间和输入/输出灵敏度。“当包括所有不确定性来源时，三名受试者的压缩负荷预测显示置信区间为3336248 N，4086333 N和3796244 N。对于77%、83%和76%的站立阶段，测得的载荷位于预测的5-95%置信区间内。肌肉最大等长力、肌肉几何形状和标记放置不确定性对关节载荷结果影响最大。这项研究表明，这些参数的识别是至关重要的，当特定主题的模型开发。
Musculoskeletal models are powerful tools that allow biomechanical investigations and predictions of muscle forces not accessible with experiments. A core challenge modelers must confront is validation. Measurements of muscle activity and joint loading are used for qualitative and indirect validation of muscle force predictions. Subject-specific models have reached high levels of complexity and can predict contact loads with surprising accuracy. However, every deterministic musculoskeletal model contains an intrinsic uncertainty due to the high number of parameters not identifiable in vivo. The objective of this work is to test the impact of intrinsic uncertainty in a scaled-generic model on estimates of muscle and joint loads. Uncertainties in marker placement, limb coronal alignment, body segment parameters, Hill-type muscle parameters, and muscle geometry were modeled with a global probabilistic approach (multiple uncertainties included in a single analysis). 5-95% confidence bounds and input/output sensitivities of predicted knee compressive loads and varus/valgus contact moments were estimated for a gait activity of three subjects with telemetric knee implants from the "Grand Challenge Competition." Compressive load predicted for the three subjects showed confidence bounds of 3336248 N, 4086333 N, and 3796244 N when all the sources of uncertainty were included. The measured loads lay inside the predicted 5-95% confidence bounds for 77%, 83%, and 76% of the stance phase. Muscle maximum isometric force, muscle geometry, and marker placement uncertainty most impacted the joint load results. This study demonstrated that identification of these parameters is crucial when subject-specific models are developed.