A critical review of the psychophysiology of driver fatigue.

By S. Lal, A. Craig. Biological Psychology, 2001

1,016 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/52c7c50af514b4f795b1de7803163616e287108f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Sara Lal
2. Gulbadan Sikander
3. Shahzad Anwar

In Experimental and Cognitive Psychology

1. Kenneth Sundaraj
2. Murugappan Murugappan
3. Arun Sahayadhas
4. Gamini Dissanayake
5. Sarath Kodagoda
6. Rami N. Khushaba
7. Sara Lal
8. Bao-Liang Lu

In Social Psychology

1. Gamini Dissanayake
2. Sarath Kodagoda
3. Kenneth Sundaraj
4. W. Staiano
5. Murugappan Murugappan
6. Rami N. Khushaba
7. Sara Lal
8. Arun Sahayadhas

Citations in the corpus

• Applying neural network analysis on heart rate variability data to assess driver fatigue (2011, background): “EEG has proved to be a useful human physiological measure to detect fatigue but electrodes need to be mounted on the scalp of the drivers.”
• Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, background): “Different studies have been reported on driver drowsiness detection including methods identifying physiological associations between driver drowsiness/fatigue and the corresponding patterns of the Electroencephalogram (EEG) (brain activity), Electroocculogram (EOG) (eye movement), and Electrocardiog”
• Using EEG spectral components to assess algorithms for detecting fatigue (2009, background): “The decrease of beta (fast wave) activity would increase the ratios in the algorithms, and although alpha activity, which represents the alert and relaxed state (Lal & Craig, 2001), is also considered as a fast wave activity, it has a small contribution towards changing the ratio.”
• Mental fatigue impairs physical performance in humans. (2009, background): “The effects of mental fatigue on cognitive performance (7, 8, 14, 38, 41, 66–68) and the skilled performance of drivers and air pilots (25, 36) have been extensively investigated.”
• Detecting Driver Drowsiness Based on Sensors: A Review (2012, background): “nishes [30] cycle of ap n when the onstant da States, Isra by driver 1694”
• EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation (2019, neutral): “Subjects may be suffered from an aggravated fatigue reaction in a monotonous and cumulative process [8], which has an effect on physiological indicators in turn.”