FADS: An Intelligent Fatigue and Age Detection System

Mohammad Hijji (), Hikmat Yar, Fath U Min Ullah, Mohammed M. Alwakeel, Rafika Harrabi, Fahad Aradah, Faouzi Alaya Cheikh, Khan Muhammad () and Muhammad Sajjad ()
Additional contact information
Mohammad Hijji: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Hikmat Yar: Digital Image Processing Laboratory, Islamia College Peshawar, Peshawar 25000, Pakistan
Fath U Min Ullah: Department of Software Convergence, Sejong University, Seoul 143-747, Republic of Korea
Mohammed M. Alwakeel: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Rafika Harrabi: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Fahad Aradah: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Faouzi Alaya Cheikh: The Software, Data and Digital Ecosystems (SDDE) Research Group, Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
Khan Muhammad: Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea
Muhammad Sajjad: Digital Image Processing Laboratory, Islamia College Peshawar, Peshawar 25000, Pakistan

Mathematics, 2023, vol. 11, issue 5, 1-16

Abstract: Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world road accidents due to the conditions of the drivers’ state such as drowsiness, stress, tiredness, and age during driving. In such cases, driver fatigue detection is mandatory to avoid road accidents and ensure a comfortable journey. To date, several complex systems have been proposed that have problems due to practicing hand feature engineering tools, causing lower performance and high computation. To tackle these issues, we propose an efficient deep learning-assisted intelligent fatigue and age detection system (FADS) to detect and identify different states of the driver. For this purpose, we investigated several neural computing-based methods and selected the most appropriate model considering its feasibility over edge devices for smart surveillance. Next, we developed a custom convolutional neural network-based system that is efficient for drowsiness detection where the drowsiness information is fused with age information to reach the desired output. The conducted experiments on the custom and publicly available datasets confirm the superiority of the proposed system over state-of-the-art techniques.

Keywords: artificial intelligence; age prediction; deep learning (DL); drowsiness detection; neural computing; smart surveillance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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