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首页> 外文期刊>Journal of Biomechanics >A Monte Carlo analysis of muscle force estimation sensitivity to muscle-tendon properties using a Hill-based muscle model
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A Monte Carlo analysis of muscle force estimation sensitivity to muscle-tendon properties using a Hill-based muscle model

机译:利用山地肌肉模型的肌肉力估计肌肉估计敏感性的蒙特卡罗分析

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Surface electromyography driven models are desirable for estimating subject-specific muscle forces. However, these models include parameters that come from an array of sources, thus creating uncertainty in the model-estimated force. In this study, we used Monte-Carlo simulations to evaluate the sensitivity of Hill-based model muscle forces to changes in 11 parameters in the muscle-tendon unit morphological properties and in the model force-length and force-velocity relationships. We decomposed the force variability and ranked the sensitivity of the model to the underlying parameters using the Variogram Analysis of Response Surfaces. For the analyzed running experiments and the adopted Hill model structure, our results show that the parameters are separable into four groups, where the parameters in each group have a synergistic contribution to the model global sensitivity. The first group consists of the maximum isometric force and the pennation angle. The second group contains the optimal fiber length, the tendon slack length, the tendon reference strain and the tendon shape factor. The third group contains the width and shape at the extremities of the active contractile element, along with the maximum contraction velocity and the curvature constant in the force-velocity curve. The fourth group consisted only of the force enhancement during eccentric contraction. The first two groups revealed the largest influence on the output force sensitivity. As many input parameters are difficult to measure and impact estimated forces, we propose that model estimates be presented with confidence intervals as well as inter parameter relationships, to encourage users to explicitly consider the model uncertainty. (C) 2018 Elsevier Ltd. All rights reserved.
机译:表面电学驱动的模型是理想的,用于估计特异性肌肉力。然而,这些模型包括来自来自源阵列的参数,从而在模型估计力中产生不确定性。在这项研究中,我们使用Monte-Carlo模拟来评估山的模型肌肉力的敏感性在肌腱单元形态特性和模型力 - 长度和力 - 速度关系中的11个参数变化。我们使用响应表面的变形仪分析将力变异性分解并将模型对底层参数的敏感性进行排序。对于分析的运行实验和采用的Hill模型结构,我们的结果表明,参数可分为四组,其中每个组中的参数对模型全局灵敏度具有协同贡献。第一组包括最大等距力和钢圈角度。第二组含有最佳纤维长度,肌腱松弛长度,肌腱参考应变和肌腱形状因子。第三组包含有源收缩元件的四肢的宽度和形状,以及力 - 速度曲线中的最大收缩速度和曲率常数。第四组仅包括偏心收缩期间的力量增强。前两组揭示了对产出力敏感性的最大影响。由于许多输入参数难以测量和影响估计力,我们提出了型号估计以置信区间以及参数间关系呈现,以鼓励用户明确地考虑模型不确定性。 (c)2018年elestvier有限公司保留所有权利。

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