摘要
为了有效提取表面肌电信号(SEMG)的特征,该文提出了一种基于相关性分析的改进的特征提取方法。首先用空域相关法对两路SEMG信号进行消噪预处理,然后对处理后的SEMG信号进行四尺度小波变换,并通过相关性分析提取SEMG信号的重要边缘在各尺度上的小波系数,以各尺度上的这些系数的平方和构建六维特征向量输入支持向量机分类器,对手部的多个动作进行分类。实验结果表明,基于相关性分析和小波变换构筑的特征向量结合支持向量机的方法能够以较高识别率区分伸腕、屈腕、展拳、握拳4种动作,能够得到比传统的神经网络分类器更为准确的分类结果。
In order to extract effectively the feature of SEMG signal, an improved method of feature extraction based on correlation analysis is proposed. Firstly, the paper decreases the noise included in two channel SEMG signals using spatial correlation filtering. Secondly, the paper analyzes SEMG signal after de-noising with 4-scale wavelet transformation and extract wavelet coefficient of the main fringe by arithmetic of correlation analysis. A 6-dimension eigenvector which is constructed with sum of squares of the wavelet coefficient is inputted SVM. The result shows that four movements （wrist spreads, wrist bends, hand extension, hand grasps） are successfully identified by the method of SVM combined with the eigenvector which is constructed at the condition of correlation analysis and wavelet transformation. The more precise classified results can be get than neural network sorter with this method.
出处
《电子与信息学报》
EI
CSCD
北大核心
2008年第10期2315-2319,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金(60474054)
浙江省科技计划(2007C23088)资助课题
关键词
表面肌电信号
相关性
特征提取
支持向量机
Surface ElectroMyoGraphy（SEMG）
Correlation
Feature extraction
Support Vector Machine（SVM）
