EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study
Many studies have shown that the majority of maritime accidents/incidents are attributed to human errors as the initiating cause. Efforts have been made to study human factors that can result in a safer maritime transportation. Among all techniques, Electroencephalogram (EEG) has the advantages such...
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sg-ntu-dr.10356-1459712021-01-23T20:11:10Z EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study Liu, Yisi Subramaniam, Salem Chandrasekaran Harihara Sourina, Olga Liew, Serene Hui Ping Krishnan, Gopala Konovessis, Dimitrios Ang, Hock Eng School of Mechanical and Aerospace Engineering 2017 International Conference on Cyberworlds (CW) Fraunhofer Singapore Engineering::Maritime studies Human Factors Electroencephalography Many studies have shown that the majority of maritime accidents/incidents are attributed to human errors as the initiating cause. Efforts have been made to study human factors that can result in a safer maritime transportation. Among all techniques, Electroencephalogram (EEG) has the advantages such as high time resolution, possibility to continuously monitor brain states with high accuracy, recognition of human mental workload, emotion, stress, vigilance, etc. In this paper, we designed and carried out an experiment to collect the EEG signals to study stress and sharing of the mental workload among crew members during collaboration tasks performance on the ship's bridge virtual simulator. Four maritime trainees were monitored in the experiment. Each of them had a role such as an officer on watch, captain, pilot, or steersman. The results show that the captain had the highest stress and workload. However, the other three trainees experienced low workload and stress due to shared work and responsibility. The EEG is a promising evaluation tool to be used in the human factors study in the maritime domain. National Research Foundation (NRF) Singapore Maritime Institute (SMI) Accepted version This research is supported by Singapore Maritime Institute and by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. We would like to acknowledge the final year project students of School of MAE of Nanyang Technological University and personally Lee Jian Wei for his contribution in this work. 2021-01-19T02:17:21Z 2021-01-19T02:17:21Z 2017 Conference Paper Liu, Y., Subramaniam, S. C. H., Sourina, O., Liew, S. H. P., Krishnan, G., Konovessis, D., & Ang, H. E. (2017). EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study. Proceedings of the International Conference on Cyberworlds, 64(71). doi:10.1109/CW.2017.37 9781538620892 https://hdl.handle.net/10356/145971 10.1109/CW.2017.37 2-s2.0-85043458267 64 71 en © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW.2017.37 application/pdf |
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Engineering::Maritime studies Human Factors Electroencephalography Liu, Yisi Subramaniam, Salem Chandrasekaran Harihara Sourina, Olga Liew, Serene Hui Ping Krishnan, Gopala Konovessis, Dimitrios Ang, Hock Eng EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study |
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Many studies have shown that the majority of maritime accidents/incidents are attributed to human errors as the initiating cause. Efforts have been made to study human factors that can result in a safer maritime transportation. Among all techniques, Electroencephalogram (EEG) has the advantages such as high time resolution, possibility to continuously monitor brain states with high accuracy, recognition of human mental workload, emotion, stress, vigilance, etc. In this paper, we designed and carried out an experiment to collect the EEG signals to study stress and sharing of the mental workload among crew members during collaboration tasks performance on the ship's bridge virtual simulator. Four maritime trainees were monitored in the experiment. Each of them had a role such as an officer on watch, captain, pilot, or steersman. The results show that the captain had the highest stress and workload. However, the other three trainees experienced low workload and stress due to shared work and responsibility. The EEG is a promising evaluation tool to be used in the human factors study in the maritime domain. |
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School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Liu, Yisi Subramaniam, Salem Chandrasekaran Harihara Sourina, Olga Liew, Serene Hui Ping Krishnan, Gopala Konovessis, Dimitrios Ang, Hock Eng |
format |
Conference or Workshop Item |
author |
Liu, Yisi Subramaniam, Salem Chandrasekaran Harihara Sourina, Olga Liew, Serene Hui Ping Krishnan, Gopala Konovessis, Dimitrios Ang, Hock Eng |
author_sort |
Liu, Yisi |
title |
EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study |
title_short |
EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study |
title_full |
EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study |
title_fullStr |
EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study |
title_full_unstemmed |
EEG-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study |
title_sort |
eeg-based mental workload and stress recognition of crew members in maritime virtual simulator : a case study |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/145971 |
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1690658330978025472 |