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

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.

Devising an approach for the automated restoration of shipmaster’s navigational qualification parameters under risk conditions

“…Taking into account large data sets, in real time, neural networks, in particular LSTM, are applied (step 7), which perform well in tangential tasks during the analysis and detection of fatigue of navigators in study [37]. However, compared to studies [38], this set of stages also included the analysis of video information (stage 9), by means of convolutional neural networks (CNN) and time series, which has high indicators of the ability to identify target vessels that may pose a danger.…”

“…An extension of the neural network formula: let x include not only the input but also parameters such as the state of fatigue (f), the level of attention (a), and the available attentional load (l). Then the extended formula of the neural network can be expressed as (37):…”