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Classification of carrier-based aircraft pilot mental workloads based on feature-level fusion and decision-level fusion of PPG and EEG signals

Published online by Cambridge University Press:  13 June 2025

W. Zhu ,
Y. Xie ,
Y. Wang ,
C. Zhang ,
J. Yuan ,
H. Chen ,
X. Zuo ,
C. Jiang Open the ORCID record for C. Jiang [Opens in a new window]  and
T. Wang
W. Zhu
Affiliation:
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China
Y. Xie
Affiliation:
Sichuan Fire Research Institute of Ministry of Emergency Management, Chengdu, China
Y. Wang
Affiliation:
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China
C. Zhang
Affiliation:
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China
J. Yuan
Affiliation:
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China School of Flight Technology, Civil Aviation Flight University of China, Guanghan, China
H. Chen
Affiliation:
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China
X. Zuo
Affiliation:
Chengdu University of Traditional Chinese Medicine, Chengdu, China
C. Jiang*
Affiliation:
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China
T. Wang
Affiliation:
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China
*
Corresponding author: C. Jiang; Email: jiangchaozhe@swjtu.edu.cn
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Abstract

This study explored mental workload recognition methods for carrier-based aircraft pilots utilising multiple sensor physiological signal fusion and portable devices. A simulation carrier-based aircraft flight experiment was designed, and subjective mental workload scores and electroencephalogram (EEG) and photoplethysmogram (PPG) signals from six pilot cadets were collected using NASA Task Load Index (NASA-TLX) and portable devices. The subjective scores of the pilots in three flight phases were used to label the data into three mental workload levels. Features from the physiological signals were extracted, and the interrelations between mental workload and physiological indicators were evaluated. Machine learning and deep learning algorithms were used to classify the pilots’ mental workload. The performances of the single-modal method and multimodal fusion methods were investigated. The results showed that the multimodal fusion methods outperformed the single-modal methods, achieving higher accuracy, precision, recall and F1 score. Among all the classifiers, the random forest classifier with feature-level fusion obtained the best results, with an accuracy of 97.69%, precision of 98.08%, recall of 96.98% and F1 score of 97.44%. The findings of this study demonstrate the effectiveness and feasibility of the proposed method, offering insights into mental workload management and the enhancement of flight safety for carrier-based aircraft pilots.

Keywords

flight safetymental workloadmulti-source data fusionmachine learningdeep learningportable device
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 20
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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Footnotes

†

Author’s notes

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