Real-time physiological and facial monitoring for safe driving

This work is to develop an intelligent driver-assistance system which can perceive the physiological state of a driver to avoid fatigue driving. The proposed system includes a camera, a wireless ElectroCardioGram (ECG) sensor patch, and a computation platform. The camera in front of a driver is to c...

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Bibliographic Details
Published in2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 4849 - 4852
Main Authors Yu-Lung Chang, Yen-Cheng Feng, Chen, Oscal T.-C
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.08.2016
Subjects
Adult
Automobile Driving
Automobiles
Electrocardiogram
Electrocardiography
Electrocardiography - methods
Face - physiology
Fatigue
Fatigue - diagnosis
fatigue driving
Female
Healthy Volunteers
Heart Rate - physiology
Heart rate variability
Humans
Male
regression method
Roads
Time-frequency analysis
Wireless Technology - instrumentation
yawning
Yawning - physiology
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Summary:This work is to develop an intelligent driver-assistance system which can perceive the physiological state of a driver to avoid fatigue driving. The proposed system includes a camera, a wireless ElectroCardioGram (ECG) sensor patch, and a computation platform. The camera in front of a driver is to catch a face image which is processed to obtain features of a mouth for identifying a yawn. The sensor patch records ECG signals which are computed to yield six Heart Rate Variability (HRV) parameters. Seven healthy subjects of 6 males and 1 female had individually driven a car, which was embedded with our system, for 3 hours at a well-known route, mostly in a freeway road. Based on the captured video and measured ECG signals, the correlations between the yawning frequency and six HRV parameters are investigated by using the regression method to discover that the ratio (LF/HF) of Low-Frequency (LF) spectrum power over High-Frequency (HF) spectrum power yields the relatively highest correlation. In order to effectively identity driver's fatigue, the variations of differential LF/HF are further characterized to attain two thresholds which are accompanied with yawning frequencies to build a fair detection mechanism. The practical road tests demonstrate that the proposed system is very feasible and easily adapted to different drivers.
ISSN:1557-170X
DOI:10.1109/EMBC.2016.7591813