| 刘光达,董梦坤,张守伟,许蓝予,周 葛,蔡 靖.基于 KPCA-SVM 的表面肌电信号疲劳分类研究[J].电子测量与仪器学报,2021,35(10):1-8 |
| 基于 KPCA-SVM 的表面肌电信号疲劳分类研究 |
| Research on fatigue classification of surface EMG signalbased on KPCA and SVM |
| |
| DOI： |
| 中文关键词: 表面肌电 特征降维 核主成分分析 支持向量机 |
| 英文关键词:surface electromyography feature reduction kernel principal component analysis support vector machine |
| 基金项目:国家重点研发计划（2018YFF0300806 1）、吉林省科技发展项目（20200404205YY）资助 |
|
|
|
|
| 摘要点击次数: 977 |
| 全文下载次数: 2135 |
| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
