Why the study?
Intense and monotonous mission phases remain a factor of cognitive fatigue for UAS operators despite shift work and fatigue risk management systems, impacting vigilance, attention, performance, and mission safety.
Machine learning based on physiological measures is a promising venue to estimate cognitive fatigue in UAS operators.
May inform UAS operator fatigue monitoring development; leaves open prospective ML validation before practice change.
Recent technological improvements allow UAS (Unmanned Aircraft System) operators to carry out increasingly long missions. Shift work was introduced during long-endurance missions to reduce the risk of fatigue. However, despite these short work periods and the creation of a fatigue risk management system (FRMS), the occurrence of intense and monotonous phases remains a factor of cognitive fatigue. This fatigue can have an impact on vigilance, attention, and operator performance, leading to reduce mission safety. This paper aims at presenting different ways to characterize the cognitive fatigue of UAS operators. The use of machine learning to estimate cognitive fatigue based on physiological measures is also presented as a promising venue to mitigate these issues.
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Jahanpour et al. (2020) studied this question.
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