西华大学电气与电子信息学院
纸质出版:2023
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冯凯, 董秀成, 刘栋博. 基于多域融合与特征选择的手势识别研究[J]. 传感器与微系统, 2023,(5):37-40+44.
冯凯, 董秀成, 刘栋博. 基于多域融合与特征选择的手势识别研究[J]. 传感器与微系统, 2023,(5):37-40+44. DOI: 10.13873/J.1000-9787(2023)05-0037-04.
由于手部运动类型增加,准确地对表面肌电(sEMG)信号进行分类需要更多特征。针对特征维数增加导致的特征冗余现象,提出了基于多域融合与特征选择的手势识别方法。从4个通道sEMG信号中提取时域、频域和时频域特征构成特征集。分别采用最小冗余最大相关(mRMR)、基于快速关联的过滤(FCBF)算法、ReliefF、Pearson相关系数进行特征排序,并且利用线性判别分析(LDA)与支持向量机(SVM)对12种精细手势动作进行分类。将4种特征选择与2种分类器的组合构成不同的手势识别模型,SVM-ReliefF手势识别模型分类效果优于其余7种识别模型,在提取30维特征的情况下,分类准确率为96.67%。结果表明:基于多域融合与特征选择的手势识别降维效果显著,且分类效果较好。
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