Real-time driver safety system based on detecting drowsiness and diagnosing fatigue

Abstract

The high rate of traffic accidents occurring annually has been a constant concern for decades, both from a national and global standpoint. Concerning this, a countermeasure will be constructed within this paper to produce a safety system that detects a driver’s drowsiness levels in real time. In this paper, bio-signals, namely Electromyogram (EMG), Electrocardiogram (ECG), and Electroencephalogram (EEG) signals, are used as the main parameters to identify drowsiness. As such, EMG, ECG, EEG, and heart rate radars were used simultaneously to obtain the aforementioned data, while data processing was conducted post-experimentation. To conduct the study, ten subjects were participated in the experiments, whereby the age group of the participants ranged from 20 to 55 years old, and were ensured to have prior driving experience. From the experimental data, the correlation between each sensor’s data was identified and score outputs produced from the respective sensors were compared. It was concluded that different muscle groups respond differently to increased drowsiness, warranting further investigation.

Document Type

Conference Proceeding

Date of Publication

1-1-2026

Volume

257

Publication Title

Lecture Notes on Data Engineering and Communications Technologies

Publisher

Springer

School

School of Science

Comments

’Azmin, M. S. B. M., Sulthan, S. M., & Al-Jumaily, A. (2025). Real-time driver safety system based on detecting drowsiness and diagnosing fatigue. Lecture Notes on Data Engineering and Communications Technologies, 257, 551–568. https://doi.org/10.1007/978-981-96-7749-8_36

Copyright

subscription content

First Page

551

Last Page

568

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Link to publisher version (DOI)

10.1007/978-981-96-7749-8_36