An active driver fatigue identification technique using multiple physiological features

Author

Li Shiwu ; Wang Linhong ; Yang Zhifa ; Ji Bingkui ; Qiao Feiyan ; Yang Zhongkai

Author_Institution

Coll. of Transp., Jilin Univ., Changchun, China

fYear

2011

fDate

19-22 Aug. 2011

Firstpage

733

Lastpage

737

Abstract

A system that can actively monitor the driver´s fatigue level in real time is urgently needed for the prevention of accidents. Support vector machine (SVM) technique is used to identify driver´s fatigue based on psychological features, such as EEG and ECG Driver´s fatigue is expressed as alert, mild fatigue, deep fatigue and drowsiness, and they are used as output variables of SVM model. Field experiments are carried out in JiangYan freeway to collect the required data to validate the SVM model. Results show that the model can recognize driver´s fatigue levels effectively and recognition precisions of all states are larger than 87.5%.

Keywords

electrocardiography; electroencephalography; occupational stress; physiology; road safety; support vector machines; traffic engineering computing; JiangYan freeway; SVM model; data collection; deep fatigue; driver fatigue level recognition; drowsiness; mild fatigue; psychological features; support vector machine technique; Brain modeling; Electrocardiography; Electroencephalography; Fatigue; Rhythm; Support vector machines; Vehicles; Active Identification; Driver Fatigue; Physiological Features; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on

Conference_Location

Jilin

Print_ISBN

978-1-61284-719-1

Type

conf

DOI

10.1109/MEC.2011.6025569

Filename

6025569