Driver Mental Fatigue Detection Based on Head Posture Using New Modified reLU-BiLSTM Deep Neural Network

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 8; pp. 10957 - 10969
• Main Authors: Ansari, Shahzeb, Naghdy, Fazel, Du, Haiping, Pahnwar, Yasmeen Naz
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Angular acceleration; Artificial neural networks; Biomedical monitoring; Computer vision; Crashes; deep learning model; Driver behavior; Driver behaviour; Fatigue; Head movement; head posture; Intelligent vehicles; Machine learning; Magnetic heads; mental fatigue detection; Monitoring; Motion capture; Neural networks; reLU-BiLSTM; Series (mathematics); Vehicle dynamics; Vehicles

Early detection of driver mental fatigue is one of the active areas of research in smart and intelligent vehicles. There are various methods, based on measuring the physiological characteristics of the driver utilising sensors and computer vision, proposed in the literature. In general, driver behav...