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A Hill-based EMG-driven model to estimate elbow torque during flexion and extension

机译:基于山的EMG驱动模型,用于弯曲弯曲和延伸期间的肘部扭矩

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Modeling of muscle force for a particular motion consists of wide range of approaches including Hill-based models. In this regard, An EMG-driven Hill-based model of the elbow during flexion-extension movement was developed using MATLAB software. Musculoskeletal model was composed of the linkage dynamics of the forearm and hand, EMG activation and musculoskeletal geometry. Six men and women participated in the current study, performing fully elbow flexion and extension for no and maximum load. Surface EMG electrodes were placed to detect the activity of muscles in order to be used in the musculoskeletal model. Besides that, capturing motion via cameras and analyzing it were performed in order to gain kinematic data. Model parameters of three flexors and one extensor were estimated by minimizing the sum of squared differences between the measured and estimated torques. Fmincon in the optimization Toolbox of MATLAB software was used to find the best possible and constricted parameters for model. Maximum force, tendon slack length and a subject-specific parameter, i.e. d, were the parameters which were calculated for all the muscles. Consequently, Reduction of RMS showed that considering a subject-specific parameter would help to have a better estimation. Furthermore, influence of parameter d for maximum load had a greater enhancement in the model estimation. Model couldn't estimate both flexion and extension parts in one movement for a particular subject. It should be noticed that, increasing in activity of Triceps at the middle of the movement, made us limit the whole action to only flexion. Finally, we concluded that flexion parameters couldn't predict the extension and would alter in according to variability of parameters.
机译:特定运动的肌肉力的建模包括广泛的方法,包括基于山的模型。在这方面,使用MATLAB软件开发了弯曲延伸运动期间弯管的基于EMG驱动的山坡模型。肌肉骨骼模型由前臂和手部,EMG活化和肌肉骨骼几何形状的连锁动力学组成。六名男女参加了目前的研究,表演了完全肘部屈曲和延伸,无和最大负荷。放置表面EMG电极以检测肌肉的活性,以便在肌肉骨骼模型中使用。除此之外,通过摄像机捕获运动并进行分析,以获得运动数据。通过最小化测量和估计的扭矩之间的平方差之和来估计三个屈肌和一个伸展剂的模型参数。 Matlab软件优化工具箱中的Fmincon用于找到最佳的模型和收缩参数。最大力,肌腱松弛长度和对象特异性参数,即D,是针对所有肌肉计算的参数。因此,RMS的减少表明,考虑特定的主题参数将有助于具有更好的估计。此外,参数d对最大负载的影响在模型估计中具有更大的增强。模型无法估计一个特定主题的一个运动中的屈曲和延伸部件。应该注意的是,在运动中间的三头肌的活动中增加,让我们将整个行动限制为仅屈曲。最后,我们得出结论,屈曲参数无法预测扩展并根据参数的可变性改变。

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