Citation

Abdullah, Mohd Fikri Azli and Mohamad Hanafiah, Mohamad Hizzudin and Yogarayan, Sumendra and Abdul Razak, Siti Fatimah and Azman, Afizan and Sayeed, Md. Shohel (2023) Driver fatigue detection using Raspberry-Pi. Indonesian Journal of Electrical Engineering and Computer Science, 32 (2). p. 1142. ISSN 2502-4752

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Abstract

The subject of fatigue monitoring is becoming more important in transportation and traffic management (including, for instance, the development of systems to detect and prevent driver drowsiness). People who work in offices are also susceptible to exhaustion, but there is currently no widely deployed system that is able to monitor this condition. In most cases, the driver’s eyelids will become heavy due to exhaustion after lengthy hoursof driving or in the absence of mental concentration. Typically, when the driver’s concentration begins to fade, audio alert would be provided to force the drivers awake. In recent times, drowsiness is risky since it can result in an accident. Thus, a solution has been proposed to identify driver drowsiness by comparing several algorithms to find improved accuracy and execution time. Besides, this system will alert the driver with an audible warning in the event of drowsiness is detected

Item Type:	Article
Uncontrolled Keywords:	Awake Driver drowsiness Fatigue Raspberry Pi Yawn
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 07:13
Last Modified:	31 Oct 2023 07:13
URII:	http://shdl.mmu.edu.my/id/eprint/11787

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