Drivers Drowsiness Detection For Analysis Eye Movement Using Machine Learning

Karthikeyani.T Karthikeyani.T; Dr.R. Vadivel

doi:10.56975/ijcrt.v14i2.301947

January 1, 2026INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTSOpen Access

Drivers Drowsiness Detection For Analysis Eye Movement Using Machine Learning

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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.

Implication

Research demonstrates effective eye movement analysis for detecting driver drowsiness using machine learning algorithms, suggesting improved road safety.

Abstract

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Cite This Study

Karthikeyani.T et al. (2026) studied this question.

synapsesocial.com/papers/69a91da8d6127c7a504c0b18 https://doi.org/10.56975/ijcrt.v14i2.301947

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Drivers Drowsiness Detection For Analysis Eye Movement Using Machine Learning

Key Points

Implication

Abstract

Discussion

Cite This Study

Drivers Drowsiness Detection For Analysis Eye Movement Using Machine Learning

Key Points

Implication

Abstract

Discussion

Cite This Study

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