What question did this study set out to answer?

The aim is to analyze eye movement data to detect drowsiness in drivers using machine learning techniques.

March 5, 2026INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS0 citationsOpen Access

Drivers Drowsiness Detection For Analysis Eye Movement Using Machine Learning

KKKarthikeyani.T Karthikeyani.T DVDr.R. Vadivel

Key Points

The aim is to analyze eye movement data to detect drowsiness in drivers using machine learning techniques.
Utilized machine learning algorithms to analyze eye movement patterns.
Collected data on driver eye movements under varying conditions.
Applied statistical methods to evaluate drowsiness indicators.
Successfully identified key eye movement features linked to drowsiness.
Demonstrated a significant accuracy rate in detecting drowsiness.
Highlighted the potential for reducing road accidents through early drowsiness detection.

Abstract

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