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

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 vehicle drivers due to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analyzed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organized based on input sources, which include the physiological/behavioral features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarized. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analyzed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a nonnegligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behavior videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realize quantitative, accurate, and real-time fatigue detections in OOWs on actual ships.