The analysis of hand movement distinction based on relative frequency band energy method.
The analysis of hand movement distinction based on relative frequency band energy method.
复制标题
基于相对频带能量法的手部动作识别分析
DOI：
10.1155/2014/781769
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发表时间：
2014
影响因子：
--
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Zhang Y;Wang G;Teng C;Sun Z;Wang J
为了成功开发假肢控制系统，人们进行了许多尝试，以提高表面肌电图（SEMG）信号的分类精度。然而，有效的特征提取仍然是表面肌电信号分类的最大挑战。提出了一种基于小波包分解的相对频带能量（RFBE）方法，用于多通道表面肌电信号的假肢模式识别。首先，利用小波包分解方法计算表面肌电信号各子空间的小波包能量，并利用小波包能量得到各频段的RFBE；然后，利用主成分分析（PCA）和Davies-Bouldin （DB）指数进行特征选择。最后，应用支持向量机（SVM）对表面肌电信号进行分类。我们的研究结果表明，RFBE方法适用于识别不同类型的前臂运动。与其他分类方法相比，该方法在表面肌电信号分类方面具有更高的分类精度。
For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.