Seafarer Mental Workload Assessment Using a Hybrid Deep Learning Model

• Autorzy: Jiang Ruonan , Fan Shiqi

Identyfikatory

DOI

10.12716/1001.19.01.07

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

mental workload; MWL; maritime safety; seafarers; bridge simulator; cognitive performance; deep learning; Human Factor; HF; electroencephalography; EEG

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2025
• Tom: Vol. 19 No. 1
• Strony: 55--60
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Jiang Ruonan

• The State Key Laboratory of Maritime Technology and Safety, Wuhan, China
• Wuhan University of Technology, Wuhan, China

• https://orcid.org/0009-0008-5769-9381

autor

Fan Shiqi

• The State Key Laboratory of Maritime Technology and Safety, Wuhan, China
• Wuhan University of Technology, Wuhan, China

• https://orcid.org/0000-0003-3714-7201

Bibliografia

• [1] R. Hanzu-Pazara et al., “Reducing of maritime accidents caused by human factors using simulators in training process,” J. Mar. Res., vol. 5, no. 1, Apr. 2008.
• [2] V. Ronca et al., “Neurophysiological Assessment of An Innovative Maritime Safety System in Terms of Ship Operators’ Mental Workload, Stress, and Attention in the Full Mission Bridge Simulator,” Brain Sci., vol. 13, no. 9, Sep. 2023. doi: 10.3390/brainsci13091319.
• [3] European Maritime Safety Agency (EMSA), “EMSA Facts & Figures 2022,” 2022. [Online]. Available: https://www.emsa.europa.eu/... (Accessed: Apr. 9, 2025).
• [4] R. P. E. Gordon, “The contribution of human factors to accidents in the offshore oil industry,” Reliab. Eng. Syst. Saf., vol. 61, no. 1, pp. 95–108, Jul. 1998.
• [5] International Seafarers’ Welfare and Assistance Network (ISWAN), "ISWAN launches allyship guidance to build safer cultures at sea," 2025. [Online]. Available: https://www.iswan.org.uk/news/iswan-launches-allyship-guidance-to-build-safer-cultures-at-sea/ (Accessed: May 6, 2025).
• [6] U.S. Department of Transportation, Maritime Administration (MARAD), "Every Mariner Builds A Respectful Culture (EMBARC) Standards Program," 2025. [Online]. Available: https://www.maritime.dot.gov/embarc (Accessed: May 6, 2025).
• [7] M. S. Young et al., “State of science: mental workload in ergonomics,” ERGONOMICS, vol. 58, no. 1, pp. 1–17, 2015.
• [8] M. Arenius, G. Athanassiou, and O. Sträter, “Systemic assessment of the effect of mental stress and strain on performance in a maritime ship-handling simulator,” IFAC Proc. Vol., vol. 43, no. 13, pp. 43–46, Jan. 2010.
• [9] Y. Liu et al., “EEG-based Mental Workload and Stress Recognition of Crew Members in Maritime Virtual Simulator: A Case Study,” in Proc. 2017 Int. Conf. Cyberworlds (CW), Sep. 2017, pp. 64–71.
• [10] Y. Liu et al., “Psychophysiological evaluation of seafarers to improve training in maritime virtual simulator,” Advanced Engineering Informatics, vol. 44, p. 101048, Apr. 2020.
• [11] A. Gevins et al., “Monitoring working memory load during computer-based tasks with EEG pattern recognition methods,” Hum. Factors, vol. 40, no. 1, pp. 79–91, Mar. 1998.
• [12] D. Dasari, G. Shou, and L. Ding, “ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task,”Front. Neurosci., vol. 11, p. 297, 2017.
• [13] B. Kirwan, A Guide to Practical Human Reliability Assessment, 1st ed. Boca Raton, FL: CRC Press, 1994.
• [14] A. D. Swain and H. E. Guttmann, “Handbook of human-reliability analysis with emphasis on nuclear power plant applications. Final report,” Sandia National Lab. (SNL-NM), Albuquerque, NM, USA, Tech. Rep. NUREG/CR-1278, SAND-80-0200, Aug. 1983.
• [15] D. I. Gertman et al., “The SPAR H human reliability analysis method,” in Proc. 4th Int. Top. Meet. Nuclear Plant Instrum., Control, Human-Machine Interface Technol., pp. 17–24, 2004. [Online]. Available: http://www.scopus.com/... (Accessed: Apr. 17, 2025).
• [16] A. Modares et al., “An integrated Cognitive Reliability and Error Analysis Method (CREAM) and optimization for enhancing human reliability in blockchain,” Decis. Anal., vol. 12, p. 100506, Sep. 2024.
• [17] J. C. Williams, “Heart—A Proposed Method for Achieving High Reliability in Process Operation by Means of Human Factors Engineering Technology,” Safety and Reliability, vol. 35, no. 3, pp. 5–25, Dec. 2015.
• [18] M. Celik and S. Cebi, “Analytical HFACS for investigating human errors in shipping accidents,” Accid. Anal. Prev., vol. 41, no. 1, pp. 66–75, Jan. 2009.
• [19] S.-T. Chen et al., “A Human and Organisational Factors (HOFs) analysis method for marine casualties using HFACS-Maritime Accidents (HFACS-MA),” Saf.Sci., vol. 60, pp. 105–114, Dec. 2013.
• [20] O. Soner, U. Asan, and M. Celik, “Use of HFACS–FCM in fire prevention modelling on board ships,” Saf. Sci., vol. 77, pp. 25–41, Aug. 2015.
• [21] Y. Liu et al., “EEG-based Cadets Training and Performance Assessment System in Maritime Virtual Simulator,” in Proc. 2018 Int. Conf. Cyberworlds (CW), Oct. 2018, pp. 214–220.
• [22] Y. Liu et al., “Human Factor Study for Maritime Simulator-Based Assessment of Cadets,” in Proc. ASME 35th Int. Conf. Ocean, Offshore Arctic Eng., vol. 3, New York: ASME, 2016, p. V003T02A073.
• [23] M. U. Iqbal et al., “Multi-class classification of control room operators’ cognitive workload using the fusion of eye-tracking and electroencephalography,” Comput. Chem. Eng., vol. 181, p. 108526, Feb. 2024.
• [24] C. A. Kothe and S. Makeig, “Estimation of task workload from EEG data: new and current tools and perspectives,” in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), 2011, pp. 6547–6551.
• [25] X. Hou et al., “CogniMeter: EEG-based Emotion, Mental Workload and Stress Visual Monitoring,” in Proc. 2015 Int. Conf. Cyberworlds (CW), Oct. 2015, pp. 153–160.
• [26] S. Shao et al., “EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT,” IEEE J. Biomed.Health Inform., vol. 28, no. 5, pp. 2536–2546, May 2024.
• [27] C. Berka et al., “EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks,” Aviat. Space Environ. Med., vol. 78, no. 5, Spec. Iss., pp. B231–B244, May 2007.
• [28] S. Lei and M. Roetting, “Influence of task combination on EEG spectrum modulation for driver workload estimation,” Hum. Factors, vol. 53, no. 2, pp. 168–179, Apr. 2011.
• [29] X. Li, L. Yang, and X. Yan, “An exploratory study of drivers’ EEG response during emergent collision avoidance,” J. Saf. Res., vol. 82, pp. 241–250, Sep. 2022.
• [30] B. T. Jap et al., “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl., vol. 36, no. 2, pp. 2352–2359, Mar. 2009.
• [31] G. Shou and L. Ding, “Frontal theta EEG dynamics in a real-world air traffic control task,” in Proc. 35th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Jul. 2013, pp. 5594–5597.
• [32] M. A. Hogervorst et al., “Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload,” Front. Neurosci., vol. 8, p. 322, 2014.
• [33] S. Fan et al., “Effects of seafarers’ emotion on human performance using bridge simulation,” Ocean Eng., vol. 170, pp. 111–119, Dec. 2018.

Uwagi

1. Pełne imiona podano na stronie internetowej czasopisma w "Authors in other databases."

2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).