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2024 Journal Impact Factor - 0.6
2024 CiteScore - 1.9
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
Seafarer Mental Workload Assessment Using a Hybrid Deep Learning Model
1 The State Key Laboratory of Maritime Technology and Safety, Wuhan, China
2 Wuhan University of Technology, Wuhan, China
2 Wuhan University of Technology, Wuhan, China
ABSTRACT: Human errors in maritime operations are closely linked to seafarers' mental workload; however, traditional assessment methods lack real-time neurocognitive resolution. This study introduces a novel psychophysiological framework that integrates electroencephalography (EEG) analysis with deep learning to objectively quantify seafarers' mental workload during onboard operations. A high-fidelity bridge simulator was utilized to generate critical maritime scenarios, including ship encounters, narrow channel navigation, poor visibility, and emergency responses. High-density EEG signals were analyzed to extract spectral features (Gamma, Beta, Alpha, Theta, Delta). A hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model was proposed to classify workload states of seafarers, combining Convolutional Neural Network (CNN)-extracted frequency patterns with Bidirectional Long Short-Term Memory (Bi-LSTM)-captured temporal dynamics, which achieves 96% accuracy. Furthermore, SHAP interpretability analysis indicated that Theta and Alpha frequencies are key indicators in distinguishing between high and low workloads for seafarers. These results provide a quantitative tool for cognitive assessment of seafarers in maritime training and serve as a guideline for workload allocation in ship bridge teams for shipping companies and maritime authorities.
KEYWORDS: Mental Workload (MWL), Maritime Safety, Seafarers, Bridge Simulator, Cognitive Performance, Deep Learning, Human Factor (HF), Electroencephalography (EEG)
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Citation note:
Jiang R., Fan S.: Seafarer Mental Workload Assessment Using a Hybrid Deep Learning Model. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 19, No. 1, doi:10.12716/1001.19.01.07, pp. 55-60, 2025
Authors in other databases:
Ruonan Jiang:
orcid.org/0009-0008-5769-9381
