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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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Pilot turning behavior cognitive load analysis in simulated flight.

Wen-Gang Zhou1, Pan-Pan Yu1, Liang-Hai Wu1

  • 1Flight Technology College, Civil Aviation Flight University of China, Guanghan, China.

Frontiers in Neuroscience
|October 8, 2024
PubMed
Summary
This summary is machine-generated.

Pilot cognitive load during flight turns can be identified using Heart Rate Variability (HRV) metrics. An advanced LSTM-Attention model accurately recognized varying cognitive loads, aiding pilot training and flight safety.

Keywords:
cognitive loadheart rate variabilitysafe ergonomicssimulated flightturning behavior

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Area of Science:

  • Aviation Psychology
  • Biomedical Engineering
  • Machine Learning

Background:

  • A flight experiment simulated pilot "preliminary screening" training modules to assess cognitive load during various turning tasks.
  • Real-world flight scenarios were recreated to provide a realistic experimental environment for data collection.

Purpose of the Study:

  • To identify and quantify the cognitive load associated with different turning maneuvers in simulated flight.
  • To develop and validate a machine learning model for recognizing pilot cognitive load during flight turns.

Main Methods:

  • Collected Heart Rate Variability (HRV) and flight data using a flight simulator and heart rate sensor bracelet.
  • Classified turning behaviors into climbing, descending, and level flight turns.
  • Developed a recognition model using machine learning and deep learning algorithms to analyze cognitive load.

Main Results:

  • Specific HRV indicators (e.g., RMSSD, SD1) showed a negative correlation with turning task cognitive load.
  • The LSTM-Attention model demonstrated high accuracy in recognizing varying cognitive loads, achieving an F1 score of 0.9491.

Conclusions:

  • HRV characteristics are effective for analyzing cognitive load in different flight turning tasks.
  • The LSTM-Attention model offers a valuable tool for future research on pilot cognitive load and can inform pilot training strategies.
  • Findings have significant implications for enhancing pilot training and improving overall flight safety.