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This document presents a methodology for real-time drowsiness detection using eye aspect ratio and machine learning techniques, specifically designed for monitoring operators in safety-critical tasks. The system utilizes computer vision to analyze eye patterns through video streams, achieving an average test accuracy of 94.9% with support vector machines. The approach aims to enhance alertness supervision, reduce human error, and prevent accidents in various high-risk environments.

Author

d3m

Language

EN