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A Model For Forecasting Mental Fatigue In Maritime Operations

Assessing seafarers’ mental fatigue levels helps identifying potential operational risks and the ability to simulate future scenarios can be used during planning and management, to ensure safer operational conditions. In this work, we propose a framework for modelling seafarers’ future mental fatigue levels using a combination of both physiological and environmental sensors and modeland data-based techniques. We established building blocks of this framework and presented examples of how it can be applied in different scenarios as soon as enough data is collected to feed the data-based section of the model. Once properly trained, this framework can be used not only to assess human-related operational risks but also to provide the necessary information to ensure that these issues are addressed before potential danger escalates to real accidents. MENTAL FATIGUE IN MARITIME OPERATION Safety-critical operations is an increasing concern across all industries dealing with human-machine interaction and systems. Human-related issues are the main cause of accidents in fields such as driving (Williamson et al., 2011), commercial air transport (Suraweera et al., 2013), and maritime operations (Chauvin et al., 2013). Among the most common issues, we can highlight situational awareness and human errors. The main contributing factors leading to these challenges are excessive workload, stress and fatigue, specially mental fatigue (MF). The maritime industry presents especial fatigue-related challenges connected to the intrinsic nature of maritime operations. This includes long and irregular working hours, long periods away from home, unpredictable environmental factors, and no clear separation between work and leisure. The International Maritime Organization (IMO) defines in its Guidelines on Fatigue (IMO, 2019) several factors influencing fatigue in seafarers. These factors can be categorized as seafarer-specific factors, management factors, ship-specific factors, environmental factors, and operational factors. Among the seafarer-specific factors we can highlight psychological and physiological characteristics and personal habits. Ship-specific factors cover all aspects related to the design and condition of the ship, such as ship motion and responses, level of automation and reliability, and physical comfort in accommodation and work spaces. Environmental factors include aspects such as noise, vibration, ship motion, ventilation and temperature. With so many contributing factors, monitoring and controlling MF levels in maritime operations is a complex task. Among the available option to assess MF, several subjective approaches are presented in the literature. Self assessment is the most common subjective approach and can rely on the use of questionnaires, such as Chalder Fatigue Scale (Chalder et al., 1993) and Epworth Sleepiness Scale (Johns, 1991), and sleeping diaries (Wadsworth et al., 2006). Although useful for tracking the user’s MF profile, these methods are generally not suitable for real-time applications. For more a realiable MF assessment, deterministic approaches are recommended. In this case, the use of physiological sensors, such as eye trackers, electrocardiogram (ECG), and electroencephalogram (EEG), is recognized as the best way to reliably assess MF in real-time (Sahayadhas et al., 2012). This is due to the intrinsic relation between changes in physiological signals and variations in MF levels. After establishing the required understanding about the causes and how to measure MF, a natural follow up question is how can we model the MF development. Answering this question is important since a good MF development model can be used for an early intervention or operational planning. Addressing this task is a challenging issue, due to the effect of external factors in the development of MF and how difficult it is to objectively relate these factors to their effects. In the work, we describe a framework for modelling MF deCommunications of the ECMS, Volume 34, Issue 1, Proceedings, ©ECMS Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther (Editors) ISBN: 978-3-937436-68-5/978-3-937436-69-2(CD) ISSN 2522-2414 Fig. 1. Framework for Mental Fatigue Assessment velopment, taking into account not only the seafarer’s physiological condition but also the effects of external factors. MEASURING AND MODELLING MENTAL FATIGUE SCENARIOS Being able to monitor the development of seafarers’ MF state in real-time can be beneficial for the safety of demanding maritime operations. A proper MF profile of all operators taking part in a complex operation enables a more precise risk assessment, which in turn can help preventing causalities. Let’s consider an operational scenario where a pilot is maneuvering a platform supply vessel (PSV) close to an oil rig for cargo unloading. Using a set of physiological sensors, one can monitor this pilot and perform an MF level assessment. This MF profile can be used to perform the operational risk assessment based on how long the operator stayed in a critical MF level. What a critical MF level is and how long the operator needs to be in this state to indicate operational risks need to be defined via experiments. This MF assessment framework is presented in Fig. 1 (Based on (Monteiro et al., 2020)). Although MF assessment can be a useful tool for reducing the risk of causalities, it presents a limitation regarding how early we can intervene in the operation. This limitation is due to the fact that the assessment system measures only the current state of the operator. Once a dangerous condition is assessed, measures need to be taken in order to mitigate the operational risk. This delay between assessment and action can be sufficient a time window for the assessed risk to turn into a real accident. Thus being able to anticipate the risky period is important to prepare the necessary mitigation measures on time. Supposing we can assess the operator’s MF state in the current operational scenario (S0), how can this scenario develop in the next, for example, two hours? If, after the assessment of scenario S0, the weather conditions develop to a much rougher sea state, how will the operator’s MF level be affected? If a collision between the PSV and the oil rig happens, how will this stressful situation come into play in the operator’s performance? These different external factors can make the prediction of future scenarios very hard, since the original scenario can lead to several possible future scenarios. This branching is presented in Fig. 2. Scenario S1