文献类型：期刊文献

英文题名：Research on GA-SVM Based Head-Motion Classification via Mechanomyography Feature Analysis

作者：Zhang, Yue[1];Yu, Jing[1];Xia, Chunming[1];Yang, Ke[1];Cao, Heng[1];Wu, Qing[1]

机构：[1]East China Univ Sci & Technol, Dept Mech Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：19

期号：9

外文期刊名：SENSORS

收录：;EI(收录号：20192106968901);WOS:【SCI-EXPANDED(收录号:WOS:000469766800030)】；

基金：This work is supported by the National Natural Science Foundation of China (Grant No. 91748110).

语种：英文

外文关键词：mechanomyography; genetic algorithm; support vector machine; head-motion; classification

摘要：This study investigated classification of six types of head motions using mechanomyography (MMG) signals. An unequal segmenting algorithm was adopted to segment the MMG signals generated by head motions. Three types of features (time domain, time-frequency domain and nonlinear dynamics) were extracted to construct five feature sets as candidate datasets for classification analysis. Genetic algorithm optimized support vector machine (GA-SVM) was used to classify the MMG signals. Three different kernel functions, different combinations of feature sets, different number of signal channels and training samples were selected for comparative analysis to evaluate the classification accuracy. Experimental results showed that the classifier had the best overall classification accuracy when using the radial basis function (RBF). Any combination of three different types of feature sets guaranteed an average accuracy of over 80%. In the case of the best combination (feature set 2 + 3 + 5), the classification accuracy was up to 88.2%. Using four channels to acquire MMG signal and no less than 60 training samples can assure a satisfactory classification accuracy.