特征与分类算法在基于肌音信号的头部运动分类中的对比研究

Comparative Study of Features and Classification Algorithms in Mechanomyogra⁃phy Based Head Movement Classification

提取颈部肌肉的肌音(Mechanomyography,MMG)信号时域、时⁃频域和非线性动力学的15个常见特征,按照其性质分为5个特征集,并选择其中一部分构建高维特征矢量后进行主成分分析(Principal component analysis,PCA)降维处理,应用于头部动作的模式识别研究中。分别采用支持向量机(Support vector machine,SVM)、K近邻(K⁃nearest neighbor,KNN)和线性判别分析(Linear discriminant analysis,LDA)3种分类器,对6种头部动作(低头、抬头、左摆头、右摆头、左转头和右转头)的MMG信号进行分类。实验结果表明,选用时域、时⁃频域和非线性动力学特征组合的方式,以及使用SVM作为分类器,可使各类动作的分类精度均达到80%以上,从而获得相对较高的准确率。

Fifteen typical features in time domain,time-frequency domain and non-linear dynamic are extracted from the mechanomyogarphy(MMG)signals in neck muscles.They are divided into five feature sets according to their nature,and part of them are constructed to high-dimension feature vectors before reducing the dimension by principal component analysis(PCA),which are applied in the pattern research for head movements.The MMG of six head movements(forward,backward,swing to left,swing to right,turn to light,turn to right)are classified by adopting three sorts of classifiers,which are support vector machine(SVM),K nearest neighbor(KNN)and linear discriminant analysis(LDA).Experimental results show that selecting the method of combining features in time domain,time-frequency and non-linear dynamic,and adopting SVM as the classifier can improve the classification accuracy up to higher than 80%in each movement,thus acquiring relatively higher rate.