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System identification of non-linear, dynamic EMG-torque relationship about the elbow

机译:肘部非线性,动态肌电转矩关系的系统辨识

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The surface electromyogram (EMG) from biceps/triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linearon-linear model structures and system identification techniques. EMG-torque performance was improved by: advanced (i.e., whitened, multiple-channel) EMGσ processors; longer duration training sets (52 s vs. 26 s); and determination of model parameters via the use of the pseudo-inverse and ridge regression methods. Best performance provided an error of 4.65% maximum voluntary contraction (MVC) flexion.
机译:来自33名受试者的二头肌/肱三头肌的表面肌电图(EMG)与肘部扭矩,对比EMG振幅(EMGσ)估计处理器,线性/非线性模型结构和系统识别技术有关。通过以下方式提高了EMG扭矩性能:先进的(即,变白的多通道)EMGσ处理器;持续时间更长的训练集(52 s和26 s);通过使用伪逆和脊线回归方法确定模型参数。最佳性能可提供最大自愿收缩(MVC)屈曲4.65%的误差。

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