Abstract
Fatigued pilots are prone to experience cognitive disorders that degrade their performance and adherence to high safety standards. In light of the current challenging context in aviation, we report the early phase of our ongoing project on the re-evaluation of human factors research for flight crew. Our motivation stems from the need for aviation organisations to develop decision support systems for operational aviation settings, able to feed-in in the organisations’ fatigue risk management efforts. Key criteria to this end are the need for the least possible intrusiveness and the added information value for a safety system. Departing from the problems in compliance-focused fatigue risk management and the intrusive nature of clinical studies, we report a neuroscientific methodology able to yield markers that can be easily integrated in a decision support system at the operational level. Reporting the preliminary phase of our live project, we evaluate the tools suitable for the development of a system that tracks subtle pilot states, such as drowsiness and micro-sleep episodes.
Highlights
Fatigue, stress and other conditions can lead to micro-sleep episodes and subtle pilot incapacitation, meaning the progressive deterioration of a pilot’s state, which “escapes the normal pilot medical screening” [1,2]
Cogscreen AM was initially designed by the FAA based on military pilots, applications took place in samples of commercial pilots reporting a clear prediction of cockpit performance [18]
A pilot’s capacity for information processing is radically decreased and their attention level diminishes, which may result in erroneous responses to crucial flight tasks
Summary
Stress and other conditions (e.g. mild stroke) can lead to micro-sleep episodes and subtle pilot incapacitation, meaning the progressive deterioration of a pilot’s state, which “escapes the normal pilot medical screening” [1,2]. These conditions are likely to impact sleep quality in terms of inability to fall asleep (prolonged sleep onset), frequent awakenings and non-invigorating sleep associated with attenuation of sleep depth (shorter slow wave activity duration). The proposed framework aims to elicit risk factors that are associated with fatigue patterns and to provide the appropriate mitigation approaches through non-pharmacological interventions. Developments regarding machine learning recommendation systems and potential intervention approaches are described
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