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首页> 外文期刊>Journal of Zhejiang University. Science, A >Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification
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Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

机译：基于EMD-AR策略的特征提取与基于LS-SVM的多分类器在肌电运动分类中的联合应用

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摘要

This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic (sEMG) signals. In contrast to the existing methods, considering the non-stationary and nonlinear characteristics of EMG signals, to get the more separable feature set, we introduce the empirical mode decomposition (EMD) to decompose the original EMG signals into several intrinsic mode functions (IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines (LS-SVMs), the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore, compared with other classifiers using different features, the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.
机译：本文通过分析表面肌电（sEMG）信号，提出了一种有效且高效的特征提取与多类分类器相结合的运动分类方法。与现有方法相比，考虑到EMG信号的非平稳和非线性特性，为了获得更可分离的特征集，我们引入了经验模式分解（EMD）将原始EMG信号分解为几个固有模式函数（IMF）然后计算每个IMF的自回归模型的系数以形成特征集。基于最小二乘支持向量机（LS-SVM），设计并构造了多类分类器以对各种运动进行分类。对比实验的结果表明，所描述的分类方案提高了运动识别的准确性。此外，与使用不同功能的其他分类器相比，出色的性能表明将EMD-AR内核嵌入运动分类的SVM技术的潜力。

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