Incorporation of seafarer psychological factors into maritime safety assessment

Psychological factors have been a critical cause of human errors in sectors such as health and aviation. However, there is little relevant research in the maritime industry, even though human errors significantly contribute to shipping accidents. It becomes even more worrisome given that seafarers are changing their roles onboard ships due to the growth of automation techniques in the sector. This research pioneers a conceptual framework for assessing seafarer psychological factors using neurophysiological analysis. It quantitatively enables the psychological factor assessment and hence can be used to test, verify, and train seafarers' behaviours for ship safety at sea and along coasts. A case study on ship collision avoidance in coastal waters demonstrates its feasibility using ship bridge simulation. An experimental framework incorporating neurophysiological data can be utilised to effectively evaluate the contribution of psychological factors to human behaviours and operational risks. Hence, it opens a new paradigm for human reliability analysis in a maritime setting. This framework provides insights for reforming and evaluating operators’ behaviours on traditionally crewed ships and in remote-controlled centres within the context of autonomous ships. As a result, it will significantly improve maritime safety and prevention of catastrophic accidents that endanger oceans and coasts

Detecting Mental Fatigue in Vessel Pilots Using Deep Learning and Physiological Sensors

Nowadays, human related issues are the main causes of accidents in the maritime domain. Among these issues, mental fatigue is responsible for reducing cognitive capabilities, situational awareness, and decision-making skills. Early detection and assessment of mental fatigue can be used to reduce the number of causalities, to the benefit of crewmembers, ship owners, and the maritime environment. Although the use of physiological sensors is the most trusted approach for measuring mental fatigue, it is a complex task due to the different ways mental fatigue can manifest in different people. In this paper, we present the application of deep learning techniques and physiological sensors to assess mental fatigue in the maritime domain, using a vessel piloting task as case study. The results demonstrate that because of their ability to extract features otherwise hard to recognize from in data, deep learning techniques in special convolutional neural networks can achieve high levels of mental fatigue classification accuracy, although cross-subject classification performance is still not sufficient for real-life applications