基于肌音信号的头部动作模式识别

肌音信号（MMG）是一种肌肉收缩时发出的低频信号，通过测量分析颈部前后两侧的胸锁乳突肌和头夹肌的肌音信号，成功识别点头、抬头、左摆、右摆、左转、右转6个头部动作模式。实验中采集了4个通道的数据，经滤波、归一化的预处理后，用不等长分割法分割出动作帧。提取了动作帧的小波包系数能量及双谱对角切片特征，经主元分析法（PCA）和Fisher线性判别分析（FLDA）降维，用支持向量机（SVM）分类。最后对小波包系数能量和双谱对角切片特征进行FLDA降维，识别率达95.92%。

Pattern Recognition of Head Movement Based on Mechanomyographic Signal

Mechanomyography (MMG) is a low frequency signal when muscle is contracted.Four channel MMG signals are collected from the sternocleidomastoid (SCM) muscles and splenius capitis (SPL) muscles in the subjects'' neck when they bowed head,raised head,bent side to left,bent side to right,turned to left,and turned to right,i.e.,six action modes,which could be successfully recognized.The four channel MMG signals were then filtered,normalized,and divided using unequal length segmentation algorithm.After extracting the energy features of wavelet packet coefficients and the feature of diagonal slices of spectrum,the dimension of features were reduced by principal component analysis (PCA) or fisher linear discriminant analysis (FLDA).Finally,all the features were classified by SVM classifier.When the features of wavelet packet coefficients energy and diagonal slices of spectrum went through FLDA dimension reduction,the recognition rate were up to 95.92%.