A new approach for a safe car assistance system

TL;DR

This paper presents a novel driver drowsiness detection system combining physiological signals and eye blinking analysis using video and EEG data, aiming to enhance road safety.

Contribution

It introduces a new multi-modal approach integrating EEG and eye blink detection with fuzzy logic classification for early drowsiness detection.

Findings

Effective detection of drowsiness using combined EEG and eye blinking signals

Utilization of fuzzy logic improves classification accuracy

System demonstrates potential for real-time driver monitoring

Abstract

Drowsiness, which is the state when drivers do not have scheduled breaks while traveling long distances, is the main reason behind serious motorway accidents. Accordingly, experts claim that drowsy state is hard to be recognized early enough to prevent serious accidents that may lead even to road deaths. In this work, we propose a new drowsiness state detection system based on physiological signals and eye blinking. An experiment has been directed to justify the utility of the proposed approach. This system uses a smart video camera that takes drivers faces images and supervises the eye blink (open and close); also, it uses the Emotiv EPOC headset to acquire the electroencephalogram (EEG) signals. Eye detection is done by Viola and Jones technique, EEG. Finally, we have chosen the fuzzy logic techniques to classify the EEG signals and eye blinking detection to analyze the results.