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%.