Experimental study of fatigue degree quantification for multi-feature fusion identification  

作　　者：孙伟 Zhu Jiandong Zhang Xiaorui He Jun Zhang Weigong 

机构地区：[1]School of Information and Control,Nanjing University of Information Science & Technology [2]School of Electronic and Information Engineering,Nanjing University of Information Science & Technology [3]School of Instrument Science and Engineering,Southeast University

出　　处：《High Technology Letters》2014年第2期146-153,共8页高技术通讯（英文版）

基　　金：Supported by the National Nature Science Foundation of China(No.61304205,61203273,61103086,41301037);the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems,Beihang University(No.BUAA-VR-13KF-04);Jiangsu Ordinary University Science Research Project(No.13KJB120007);Innovation and Entrepreneurship Training Project of College Students(No.201410300153,201410300165)

摘　　要：A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the reaction and operation abilities of drivers about traffic signals.By comparison experiment with that EEG signal based,multivariate statistical analysis and fusion identification based on BP neural network(BPNN) results show that the experimental procedure is simple and practical,and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory,and also can achieve accurate and reliable quantification of fatigue degree,especially under the associated action of multiple fatigue feature parameters.A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications. Using the fatigue degree test software, fatigue degree is objectively quanti- fied by analyzing the reaction and operation abilities of drivers about traffic signals. By comparison experiment with that EEG signal based, multivariate statistical analysis and fusion identification based on BP neural network （ BPNN） results show that the experimental procedure is simple and practical, and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory, and also can achieve accurate and reliable quantification of fatigue degree, especially under the associated action of multiple fatigue feature parameters.

关 键 词：fatigue driving fatigue degree quantification fusion identification experimental study 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]