dc.contributor.author | Ana, Mateus Sant’ | |
dc.contributor.author | Li, Guoyuan | |
dc.contributor.author | Zhang, Houxiang | |
dc.date.accessioned | 2019-09-25T14:19:49Z | |
dc.date.available | 2019-09-25T14:19:49Z | |
dc.date.created | 2019-08-15T15:17:34Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-1-7281-1163-6 | |
dc.identifier.uri | http://hdl.handle.net/11250/2618814 | |
dc.description.abstract | Human fatigue is one of the main causes of accidents in maritime domain. How to use physiological data to estimate degree of human fatigue without medical domain knowledge is significant to the safety of tasks in maritime operations. In this paper, a decentralized sensor fusion approach is proposed. Various sensor data used to monitor brain wave, heart rate, muscle tension, body temperature, visual focus and head movement, together with subjective measurement of Karolinska Sleepiness Scale (KSS) values are selected as the data source for this study. Convolutional neural networks are adopted in the approach to extract local features of each individual data channel. The local features are further fused into a 5-layer fuzzy neural network for classification of the KSS values. A case study of fatigue monitoring test of ship maneuvering in simulator has been carried out. Through a comparative study with a centralized fusion approach, the proposed method is verified to be able to provide high accuracy up to 96.08% for fatigue level classification, and in particular, robust enough to maintain the accuracy to 88.42% in case of sensor failure. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | IEEE | nb_NO |
dc.relation.ispartof | Proceedings of the 15th IEEE International Conference on Control and Automation | |
dc.title | A decentralized sensor fusion approach to human fatigue monitoring in maritime operations | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 1569-1574 | nb_NO |
dc.identifier.cristin | 1716214 | |
dc.relation.project | Norges forskningsråd: 237929 | nb_NO |
dc.description.localcode | © 2019 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. | nb_NO |
cristin.unitcode | 194,64,93,0 | |
cristin.unitname | Institutt for havromsoperasjoner og byggteknikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |