Lyu, Hongguang;
Yue, Jingwen;
Zhang, Wenjun;
Cheng, Tao;
Yin, Yong;
Yang, Xue;
Gao, Xiaowei;
... Li, Jiawei; + view all
(2023)
Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review.
IEEE Sensors Journal
10.1109/jsen.2023.3281068.
(In press).
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Abstract
Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships.
| Type: | Article |
|---|---|
| Title: | Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/jsen.2023.3281068 |
| Publisher version: | https://doi.org/10.1109/jsen.2023.3281068 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Fatigue, Marine vehicles, Feature extraction, Vehicles, Sleep, Physiology, Accidents |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10171836 |
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