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首页> 外文期刊>BioSystems >A fuzzy-genetic model for estimating forces from electromyographical activity of antagonistic muscles due to planar lower arm movements: The effect of nonlinear muscle properties
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A fuzzy-genetic model for estimating forces from electromyographical activity of antagonistic muscles due to planar lower arm movements: The effect of nonlinear muscle properties

机译：一种模糊遗传模型，用于估计由于下臂平面运动而产生的拮抗性肌肉的肌电图活动力：非线性肌肉特性的影响

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The aim of this paper is to create a model for mapping the surface electromyogram (EMG) signals to the force that generated by human arm muscles. Because the parameters of each person's muscle are individual, the model of the muscle must have two characteristics: (1) The model must be adjustable for each subject. (2) The relationship between the input and output of model must be affected by the force-length and the force-velocity behaviors are proven through Hill's experiments. Hill's model is a kinematic mechanistic model with three elements, i.e. one contractile component and two nonlinear spring elements.In this research, fuzzy systems are applied to improve the muscle model. The advantages of using fuzzy system are as follows: they are robust to noise, they prove an adjustable nonlinear mapping, and are able to model the uncertainties of the muscle.Three fuzzy coefficients have been added to the relationships of force-length (active and passive) and force-velocity existing in Hill's model. Then, a genetic algorithm (GA) has been used as a biological search method that can adjust the parameters of the model in order to achieve the optimal possible fit.Finally, the accuracy of the fuzzy genetic implementation Hill-based muscle model (FGIHM) is invested as following: the FGIHM results have 12.4% RMS error (in worse case) in comparison to the experimental data recorded from three healthy male subjects. Moreover, the FGIHM active force-length relationship which is the key characteristics of muscles has been compared to virtual muscle (VM) and Zajac muscle model. The sensitivity of the FGIHM has been evaluated by adding a white noise with zero mean to the input and FGIHM has proved to have lower sensitivity to input noise than the traditional Hill's muscle model.
机译：本文的目的是创建一个模型，用于将表面肌电图（EMG）信号映射到人手臂肌肉产生的力。因为每个人的肌肉的参数都是个体的，所以肌肉的模型必须具有两个特征：（1）该模型对于每个对象都必须是可调整的。 （2）模型的输入和输出之间的关系必须受力长度的影响，力-速度行为通过希尔的实验证明。希尔模型是一种运动力学模型，具有三个要素，即一个可收缩分量和两个非线性弹簧要素，在本研究中，应用模糊系统来改进肌肉模型。使用模糊系统的优点如下：它们对噪声具有鲁棒性，它们证明了可调节的非线性映射，并且能够对肌肉的不确定性进行建模。在力长关系（主动和主动）之间增加了三个模糊系数希尔模型中存在被动和力-速度。然后，将遗传算法（GA）用作生物搜索方法，该算法可以调整模型的参数以实现最佳的可能拟合。最后，模糊遗传实现基于希尔的肌肉模型（FGIHM）的准确性的投资如下：与三个健康男性受试者记录的实验数据相比，FGIHM结果的RMS误差为12.4％（在更糟的情况下）。此外，已经将作为肌肉关键特征的FGIHM主动力-长度关系与虚拟肌肉（VM）和Zajac肌肉模型进行了比较。 FGIHM的灵敏度已通过向输入中添加均值为零的白噪声进行了评估，并且事实证明，与传统的Hill肌肉模型相比，FGIHM对输入噪声的灵敏度更低。

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