Drowsiness Detection and Alarming System

Abstract

Drowsiness detection systems are crucial in enhancing safety by preventing accidents caused by fatigue, particularly in high-risk environments like road transport, aviation, and industrial operations. These systems typically rely on various physiological and behavioral indicators to assess the alertness of an individual. Several studies highlight the use of eye and facial feature tracking to detect signs of drowsiness such as prolonged eye closure, blinking patterns, and head nodding. Other research integrates multi-modal signals, combining facial features with physiological data such as heart rate or EEG signals, for a more robust detection system. Machine learning models, particularly in conjunction with real-time data processing, have been shown to improve the accuracy of these systems by learning individual patterns and reducing false positives.