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dc.contributor.authorMonteiro, Thiago Gabriel
dc.contributor.authorLi, Guoyuan
dc.contributor.authorSkourup, Charlotte
dc.contributor.authorZhang, Houxiang
dc.date.accessioned2020-06-22T07:45:18Z
dc.date.available2020-06-22T07:45:18Z
dc.date.created2020-06-21T16:55:31Z
dc.date.issued2020
dc.identifier.citationSensors. 2020, 20 (9), .en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2658943
dc.description.abstractHuman-related issues are currently the most significant factor in maritime causalities, especially in demanding operations that require coordination between two or more vessels and/or other maritime structures. Some of these human-related issues include incorrect, incomplete, or nonexistent following of procedures; lack of situational awareness; and physical or mental fatigue. Among these, mental fatigue is especially dangerous, due to its capacity to reduce reaction time, interfere in the decision-making process, and affect situational awareness. Mental fatigue is also especially hard to identify and quantify. Self-assessment of mental fatigue may not be reliable and few studies have assessed mental fatigue in maritime operations, especially in real time. In this work we propose an integrated sensor fusion system for mental fatigue assessment using physiological sensors and convolutional neural networks. We show, by using a simulated navigation experiment, how data from different sensors can be fused into a robust mental fatigue assessment tool, capable of achieving up to 100% detection accuracy for single-subject classification. Additionally, the use of different sensors seems to favor the representation of the transition between mental fatigue states.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleInvestigating an Integrated Sensor Fusion System for Mental Fatigue Assessment for Demanding Maritime Operationsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber18en_US
dc.source.volume20en_US
dc.source.journalSensorsen_US
dc.source.issue9en_US
dc.identifier.doi10.3390/s20092588
dc.identifier.cristin1816493
dc.description.localcodeThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal