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To evaluate pilot mental workload across different flight phases using heart rate variability features and machine learning methods.

Pilot mental workload analysis in the A320 traffic pattern based on HRV features

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To evaluate pilot mental workload across different flight phases using heart rate variability features and machine learning methods.
Conducted simulated A320 traffic pattern flight missions
Collected heart rate data and multidimensional task assessments
Trained classifiers such as RF for workload classification
XGBoost model improved accuracy from 50.09% to 66.67%, an increase of 16.58%
F1-score rose from 37.63% to 58.33% after feature selection
Identified HRV suppression during high-workload phases like landing
Observed HRV recovery in low-workload phases such as cruise

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Pilot mental workload assessment during takeoff and climb using heart rate variability and machine learning suggests effective monitoring strategies.

Yuan et al. (2025) studied this question.

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Pilot mental workload assessment during takeoff and climb using heart rate variability and machine learning suggests effective monitoring strategies.