Citation

Rahman A, Hriday MBH, Khan R. Heliyon 2022; 8(10): e11204.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.heliyon.2022.e11204

PMID

36325144

PMCID

PMC9619001

Abstract

The fatality of road accidents in this era is alarming. According to WHO, approximately 1.30 million people die each year in road accidents. Road accidents result in significant socioeconomic losses for people, their families, and the country. The integration of modern technologies into automobiles can help to reduce the number of people killed or injured in road accidents. Most of the study and police reports claim that fatigued driving is one of the deadliest factors behind many road accidents. This paper presents a complete embedded system to detect fatigue driving using deep learning, computer vision, and heart rate monitoring with Nvidia Jetson Nano developer kit, Arduino Uno, and AD8232 heart rate module. The proposed system can monitor the driver's real-time situations, then analyze the situation to detect any fatigue conditions and act accordingly. The onboard camera module constantly monitors the driver. The frames are retrieved and analyzed by the core system that uses deep learning and computer vision techniques to verify the situation with Nvidia Jetson Nano. The driver's states are identified using eye and mouth localization approaches from 68 distinct facial landmarks. Experimentally driven threshold data is employed to classify the states. The onboard heart rate module constantly measures the heart rates and detects any fluctuation in BPM related to the drowsiness. This system uses a convolutional neural network-based deep learning framework to include additional face mask detection to cope with the current pandemic situation. The heart rate module works parallelly where the other modules work in a conditional sequential manner to ensure uninterrupted detection. It will detect any sign of drowsiness in real-time and generate the alarm. The system successfully passed the initial lab tests and some actual situation experiments with 97.44% accuracy in fatigue detection and 97.90% accuracy in face mask identification. The automatic device was able to analyze different situations of drivers (different distances of driver from the camera, various lighting conditions, wearing eyeglasses, oblique projection) more precisely and generate an alarm before the accident happened.

Language: en

Keywords

Fatigue; Eye tracking; Convolutional neural network; Drowsiness detection; Embedded system; Eye aspect ratio; Nvidia Jetson Nano; Percentage of eye closure