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Development of a Headband for Acquisition and Analysis of Forehead EOG Signal for Driver Fatigue Detection based on Eye-Blink Patterns

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 23
Year of Publication: 2014
Anil P. C.
Aravind B.
Saritha S.
George Varkey

Anil P C., Aravind B., Saritha S. and George Varkey. Article: Development of a Headband for Acquisition and Analysis of Forehead EOG Signal for Driver Fatigue Detection based on Eye-Blink Patterns. International Journal of Computer Applications 96(23):42-46, June 2014. Full text available. BibTeX

	author = {Anil P. C. and Aravind B. and Saritha S. and George Varkey},
	title = {Article: Development of a Headband for Acquisition and Analysis of Forehead EOG Signal for Driver Fatigue Detection based on Eye-Blink Patterns},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {23},
	pages = {42-46},
	month = {June},
	note = {Full text available}


Road accident occupies the number two position (next to heart attack) among the causes for sudden casualties in India. And it is the cause for 35. 2 % of total accident deaths happened in 2012 [1]. Therefore, efforts for prevention of such accidents can lead to significant social and economical benefits. Body area network with wearable computing is an emerging field that can augment current practices for driver safety and accident prevention. It provides the possibility of continuous monitoring of the biological signals for detection of driver fatigue for on-line alerts. This paper presents our experience with development of a low cost system for this. It consists of a head-band housing EOG sensors connected to a small control system developed using open source hardware and the analysis software running inside the mobile phone of the driver. Detailed analysis of multi-channel EOG signal can give a number of indications of the level of driver fatigue. Unfortunately, factors like baseline drift, signal contamination, etc. , make the real time analysis difficult. However, eye-blink patterns in the EOG signal stream are easy to detect and the methods for this are generally immune to many of the above problems. Our system is able to accurately capture 89% of the eye-blinks in controlled experiments where the subject is watching a video clip on a computer screen. The majority of cases where the eye-blinks were not correctly identified were the ones in which the blink event got immersed in the characteristic signal for simultaneous eye movement. These could be classified and characterized separately. Further work with field study is needed to establish efficacy of the method and modifications required to make it operationally acceptable.


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