Recognition of marine navigators’ workload based on eye movement features using bridge simulation

The human factor accounts for 80% of marine traffic accidents, either directly or indirectly. Workload significantly impacts the reliability of marine navigators’ behavior. Employing eye movement measurement as an objective assessment tool can effectively evaluate seafarers’ workload. This study conducts bridge simulation experiments to analyze eye movement features and investigate marine navigators’ workload levels across different tasks. By categorizing marine navigators’ workload into high and low level based on specific navigation duties. The study employs an independent sample T-test to select blink frequency, fixation time percentage, and pupil area standard deviation as key eye movement features for workload recognition. A marine navigators’ workload recognition model is developed utilizing the support vector machine (SVM) algorithm, which is subsequently trained and evaluated with experimental data. Results demonstrate a high recognition rate of 90.91% for marine navigators’ workload, indicating the SVM method’s accuracy in workload assessment based on eye movement features. These findings lay the foundation for the advancement of intelligent monitoring systems for marine navigators’ workload and the implementation of human–computer interaction active early warning technology and equipment.

• Record URL:
  • https://doi.org/10.1016/j.ssci.2024.106607
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0925753524001978
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09257535
• Supplemental Notes:
  • © 2024 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Ma, Yue
  • Liu, Qing
  • 0000-0001-6013-7744
  • Yang, Liu
  • He, Meng
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 106607
• Serial:
  • Safety Science
  • Volume: 178
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0925-7535
  • Serial URL: http://www.sciencedirect.com/science/journal/09257535

Subject/Index Terms

• TRT Terms: Driver monitoring; Eye movements; Human factors in crashes; Ship navigators; Workload
• Subject Areas: Marine Transportation; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01926010
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 30 2024 9:55AM