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Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

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A comprehensive prediction and evaluation method of pilot workload.

Chuanyan Feng¹, Xiaoru Wanyan¹, Kun Yang²

¹School of Aeronautics Science and Engineering, Beihang University, Beijing 100191, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|May 2, 2018

Summary

Pilot workload prediction is crucial for aviation safety. This study used physiological data like eye movements and heart rate in a flight simulator, achieving 84.85% accuracy in predicting pilot workload.

Keywords:

Workload fixation frequency human factor multinominal logistic regression physiological measurement

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Area of Science:

Human Factors Engineering
Aerospace Medicine
Cognitive Psychology

Background:

Pilot workload prediction is a critical challenge in ensuring cockpit human factor airworthiness.
Accurate workload assessment is essential for flight safety and operational efficiency.

Purpose of the Study:

To design a pilot traffic pattern task within a flight simulation environment.
To develop and enhance methods for pilot workload prediction and evaluation.

Main Methods:

Utilized multiple resource theory to predict workloads for typical flight subtasks (cruise, approach, landing).
Validated prediction models by correlating sensitive physiological data with predicted workload values.
Collected eye movement, Electrocardiogram (ECG), and Electrodermal Activity (EDA) data.

Main Results:

Eye movement indices (fixation frequency, mean fixation time, saccade frequency, mean saccade time, mean pupil diameter) were sensitive to workload.
ECG indices (mean normal-to-normal interval, LF/HF ratio) and EDA indices (mean tonic, mean phasic) also showed sensitivity to typical pilot workloads.
Physiological data effectively reflected varying levels of cognitive demand during flight tasks.

Conclusions:

A multinomial logistic regression model was developed using a combination of key physiological indices.
The model integrated fixation frequency, mean normal-to-normal interval, LF/HF ratio, and mean tonic for workload prediction.
The combined physiological index model achieved a high discriminant accuracy of 84.85% for workload evaluation.