基于 KPCA-SVM 的表面肌电信号疲劳分类研究

为了提高手臂疲劳模型识别的准确率,本研究在常用时域、频域特征的基础上,引入了时频域、非线性和参数模型特征,提取3通道的表面肌电信号,构成特征集合.特征降维一般分为特征提取以及特征选择,分别采用特征提取中的主成分分析(PCA),核主成分分析(KPCA)方法以及特征选择中的互信息(MI)度量方法进行特征降维,采用支持向量机(SVM)和K近邻(KNN)作为分类器,通过3种降维方法分与SVM和KNN的不同组合构成疲劳分类模型.结果 表明,KPCA与SVM的组合模型对于疲劳的正确识别率最高达到99％,高于其他组合算法.

Research on fatigue classification of surface EMG signal based on KPCA and SVM

In order to improve the accuracy of arm fatigue model recognition, this study introduces time-frequency domain, nonlinearity and parametric model features based on common time-domain and frequency-domain features, and extracts 3-channel surface EMG signals to form features set. Feature dimensionality reduction is generally divided into feature extraction and feature selection. This research uses principal component analysis ( PCA) in feature extraction, kernel principal component analysis ( KPCA) and mutual information (MI) measurement methods in feature selection. Feature dimensionality reduction, using support vector machine ( SVM) and K-nearest neighbor (KNN) as the classifier; three dimensionality reduction methods and different combinations of SVM and KNN constitute a fatigue classification model. Results show that the correct recognition rate of KPCA and SVM is 99%, which is higher than other combination algorithms.