Mathematical models of human paralyzed muscle after long-term training.
Mathematical models of human paralyzed muscle after long-term training.
复制标题
人体长期训练后瘫痪肌肉的数学模型。
DOI：
10.1016/j.jbiomech.2006.12.015
复制
发表时间：
2007
影响因子：
2.4
通讯作者：
Shields,RK
中科院分区：
文献类型：
--
作者：
Law,LAFrey;Shields,RK
脊髓损伤（SCI）导致主要的肌肉骨骼适应，包括肌肉萎缩、更快的收缩特性、增加的疲劳性和骨丢失。功能性电刺激（FES）的使用提供了一种防止麻痹肌肉适应的方法，以维持力量产生能力。数学肌肉模型可能能够预测FES期间的最佳激活策略，然而肌肉特性进一步适应长期训练。本研究的目的是比较三种肌肉模型的准确性，一个线性和两个非线性，预测瘫痪比目鱼肌力量暴露于长期FES训练后。此外，我们对比了训练和未经训练的肢体之间的结果。三个模型的参数最适合于训练的比目鱼肌（N=4）中的单个力训练。九个额外的力列车（测试列车）预测每个主题使用开发的模型。预测和实验力列之间的模型误差进行了确定，包括特定的肌肉力量属性。线性模型的平均总误差最大（15.8%），非线性Hill Huxley型模型的平均总误差最小（7.8%）。训练与未训练的肢体之间没有观察到显着的误差差异，虽然模型参数值显着改变与培训。这项研究证实，非线性模型最准确地预测训练和未经训练的瘫痪肌肉力量属性。此外，优化的模型参数值响应于瘫痪肌肉的相对生理状态（训练与未训练）。这些研究结果与SCI患者神经假体装置的设计和控制有关。
Spinal cord injury (SCI) results in major musculoskeletal adaptations, including muscle atrophy, faster contractile properties, increased fatigability, and bone loss. The use of functional electrical stimulation (FES) provides a method to prevent paralyzed muscle adaptations in order to sustain force-generating capacity. Mathematical muscle models may be able to predict optimal activation strategies during FES, however muscle properties further adapt with long-term training. The purpose of this study was to compare the accuracy of three muscle models, one linear and two nonlinear, for predicting paralyzed soleus muscle force after exposure to long-term FES training. Further, we contrasted the findings between the trained and untrained limbs. The three models’ parameters were best fit to a single force train in the trained soleus muscle (N=4). Nine additional force trains (test trains) were predicted for each subject using the developed models. Model errors between predicted and experimental force trains were determined, including specific muscle force properties. The mean overall error was greatest for the linear model (15.8%) and least for the nonlinear Hill Huxley type model (7.8%). No significant error differences were observed between the trained versus untrained limbs, although model parameter values were significantly altered with training. This study confirmed that nonlinear models most accurately predict both trained and untrained paralyzed muscle force properties. Moreover, the optimized model parameter values were responsive to the relative physiological state of the paralyzed muscle (trained versus untrained). These findings are relevant for the design and control of neuro-prosthetic devices for those with SCI.