Enhanced EMG signal processing for simultaneous and proportional myoelectric control
Enhanced EMG signal processing for simultaneous and proportional myoelectric control
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DOI：
10.1109/iembs.2009.5332745
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发表时间：
2009-11
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通讯作者：
Johnny L. G. Nielsen;Steffen Holmgaard;N. Jiang;K. Englehart;D. Farina;P. Parker
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文献类型：
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作者：
Johnny L. G. Nielsen;Steffen Holmgaard;N. Jiang;K. Englehart;D. Farina;P. Parker
提出了一种从多通道表面肌电信号中提取神经控制信息的信号处理方法。提取的信息可用于多自由度假肢的比例控制。从多通道表面肌电信号中提取了4个时域（TD）特征，这些特征涉及手腕的三个自由度同时被激活。在这些收缩过程中，三个手腕自由度产生的力也使用定制的力传感器收集。提取的特征和记录的力信号作为输入/目标对，然后用于训练多层感知器（MLP）神经网络。采用五重交叉验证训练/测试方法。与先前提出的用于比例多自由度肌电控制任务的表面肌电信号处理方法相比，该方法的性能有了显著改善。
A new signal processing scheme is presented for extracting neural control information from the multi-channel surface electromyographic signal (sEMG). The extracted information can be used to proportionally control a multi-degree of freedom (DOF) prosthesis. Four time-domain (TD) features were extracted from the multi-channel sEMG during a series of anisotonic, isometric wrist contractions, which involved simultaneous activations of the three DOF of the wrist. The forces produced at the three wrist DOFs during these contractions were also collected using a customized force sensor. The extracted features and the recorded force signals, as input/target pairs, were then used to train a multilayer perceptron (MLP) neural network. A five-fold cross-validation training/testing method was applied. The resulting performance is a significant improvement over a previously proposed sEMG processing method for the proportional, multi-DOF myoelectric control task.