The impact of marine engine noise exposure on seafarer fatigue: A China case

Cui, Renda; Liu, Zhengjiang; Wang, Xinjian; Yang, Zaili; Fan, Shiqi; Shu, Yu

doi:10.1016/j.oceaneng.2022.112943

Cited by 12 publications

(1 citation statement)

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“…This is more likely to occur on board a ship because ship OOWs may encounter more difficult and challenging working conditions, as follows [8]: 1) 24/7 working patterns, changing time zones, and shifts (e.g., four-and six-hour shifts [9]), which result in irregular circadian rhythms and working hours, as well as disruption to the biological clock [10,11]. 2) Severe weather, rough seas, seasickness, vibrations, noise, prolonged loneliness, tension, and stress, which result in low sleep quality [12,13]. 3) Frequent port calls and the associated cargo work, excessive workloads, and reduced sleep duration [14].…”

Section: Fatigue Detection For Ship Oows Based On Input Data Features...mentioning

confidence: 99%

Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review

et al. 2023

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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.

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