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Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

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Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring.

Alexey Kashevnik¹, Svetlana Kovalenko², Anton Mamonov^3,4

¹St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg 199178, Russia.

Sensors (Basel, Switzerland)

|November 9, 2024

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This summary is machine-generated.

This study introduces a novel, open-source method for real-time mental fatigue detection using eye movement analysis. The system accurately identifies fatigue by analyzing gaze patterns, crucial for preventing accidents in critical industries.

Area of Science:

Human-Computer Interaction
Cognitive Science
Machine Learning

Background:

Current mental fatigue detection relies on subjective or indirect measures.
Existing systems lack real-time, accurate, open-source solutions for monitoring operator fatigue.
Fatigue poses significant risks in critical industries like transportation and nuclear power.

Purpose of the Study:

To develop and validate a real-time, accurate method for detecting mental fatigue using eye movement data.
To identify key gaze characteristics indicative of mental fatigue.
To provide an open-source solution for operator fatigue monitoring.

Main Methods:

Utilized a dataset of eye-tracking data from operators performing various tasks.
Developed a technique to pinpoint the most relevant gaze characteristics for fatigue detection.

Keywords:

eye-tracking machine learning mental fatigue detection

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Last Updated: Jun 8, 2025

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Applied machine learning classifiers including Random Forest, Decision Tree, and Multilayered Perceptron.

Main Results:

Identified key indicators: average fixation velocity, gaze trajectory curvature, saccade length, and fixation duration percentages.
Achieved maximum accuracy of 0.85 and F1-score of 0.80 using the Random Forest model.
Demonstrated real-time processing capability for eye movement data.

Conclusions:

The developed eye movement analysis method provides an accurate and real-time solution for mental fatigue detection.
This approach can significantly enhance safety in critical operational environments.
The identified gaze characteristics offer valuable insights into cognitive load and fatigue states.