Fatigue Risk Management System (FRMS), as defined by the International Civil Aviation Organization (ICAO), represents data-driven methods of constant monitoring, data collecting, analyzing, and mitigating fatigue-related safety risks in flight operations, using scientific methods, previous knowledge, and operational experience [1,18,19].

In Europe, fatigue management ensures that crew members are protected from excessive fatigue levels by issuing regulations called Flight Time Limitations (FTL) [9,29]. However, restrictions on working hours are different from country to country, and in various ways, they restrict the permitted flight duty, length of rest period, and other FTL elements [16,17]. Also, the prescriptive nature of these limitations prohibits some elements of crew schedules but also allows others that can be very fatigue-inducing while all regulatory provisions are complied with at the same time. Although the European regulation—FTL [9] promotes the use of the Fatigue Risk Management System (FRMS) [1,18], it does not oblige the airlines to implement it, except in certain specific cases.

At the same time, European FTL also requires the airlines [9,12] to ensure that flight duty periods are planned in a way that enables crew members to remain sufficiently alert so that they can operate to a satisfactory level of safety to appreciate the relationship between the frequencies and pattern of flight duty and rest periods and consider the cumulative effects of undertaking long duty hours interspersed with minimum rest; to allocate duty patterns that avoid such undesirable practices as alternating day/night duties or the positioning of crew members so that a serious disruption of established sleep/work patterns occurs; and to provide rest periods of sufficient duration, especially after long flights crossing numerous time zones, to enable crew members to overcome the effects of the previous duties and to be rested by the start of the following flight duty period.

Another example is USA regulations pertaining to Fatigue Risk Management Systems (FRMS) for aviation safety, which are issued by the Federal Aviation Administration (FAA). Fatigue Risk Management Systems are prescribed to ensure aviation industry personnel perform their duties safely. Information is provided regarding the components of an aviation FRMS and FRMS implementation within the aviation system, and it defines an FRMS as an operator-specific process, i.e., while all FRMSs have common elements, the specifics can be tailored to a certificate holder’s particular conditions. Detailed guidance on how to prepare for the FRMS approval process, develop the required documentation, develop and apply fatigue risk management and safety assurance processes, collect and analyze data, and develop flight crew FRMS operations procedures is also available [30].

The most commonly used measurement methods of flight crew fatigue in any FRMS include subjective fatigue scales (e.g., Samn Perelli, Karolinska), psychomotor vigilance tests, actigraphy, predictive models (biomathematical algorithms), and sleep diaries [12,19,31].

An important source of data for fatigue research, especially in flight operations, is subjective fatigue scales used in fatigue-related reporting [1]. The application of subjective scales in flight crew fatigue research can be found in numerous studies where pilots reported subjective fatigue levels using the Samn Perelli scale, such as research conducted by Powell and others in 2007 and 2008 [32,33]. Some studies involved actigraphy, sleep diaries, performance vigilance tests, and biomathematical predictive models where fatigue impact was studied using different quantification methods, such as research conducted by Powell and others in 2014, Gander and others in 2014, Van den Berg and others in 2015 [34,35,36]. Predictive models can be found in advanced crew management software, and they can warn flight crew planners on the existence of fatigue risk (usually with warning messages and/or color schemes—from green as no risk to red as high fatigue risk). Research conducted by Yi and Moochhala in 2013 showed that there is a strong correlation between certain biomarkers in saliva and fatigue levels [37]. In addition to the objectivation methods of quantifying the flight crew fatigue, cognitive abilities that deteriorate as fatigue increases, can be measured with a chronometric approach of measuring cognitive functions, i.e., an electronic CRD system of standardized chronometric cognitive tests, as per Drenovac in 2009 [38]. CRD series have been used in various studies since 1969 [38]. Instruments, methodology, measuring parameters, and other information are well explained and documented in the CRD handbook [38]. The CRD series has been used to study psychomotor disturbances of scuba divers, as per Petri in 2003 [39], and other studies showed differences between the working ability of the driver, train operator, and dispatcher during day and night shift [40,41]. A study by Meško in 2008 showed how CRD series have been used to evaluate the psychomotor abilities of military pilots [42], and some older research showed how CRD series have been used to study workload and work efficiency during certain periods [43,44]. Recent research includes several innovative approaches, such as determining sleeping patterns of flight attendants during the off-duty period using a photovoice technique, conducted by Laovoravit and others in 2019 [45]; studying new tools for use by pilots and the aviation industry to manage risks pertaining to work-related stress and wellbeing, performed by Cahill and others in 2020 [46]; analyzing aircraft pilots workload using Heart Rate Variability (HRV) and the NASA Task Load Index questionnaire, presented by Alaimo and others in 2020 [47]; applying multimodal analysis of eye movements and fatigue in a simulated glass cockpit environment, conducted by Naeeri and others in 2021 [48]; studying work type influence on air traffic controllers’ fatigue based on data-driven PERCLOS detection, conducted by Zhang and others in 2021 [49]; identifying pilots’ fatigue status based on functional near-infrared spectroscopy, conducted by Pan and others in 2022 [50]; examining fatigue during long-haul flights of different crew compositions under exemption from layover and flight time during COVID-19, conducted by Zhou and others in 2022 [51]; studying factors impacting fatigue among collegiate aviation pilots, conducted by Keller and others in 2022 [52], and examining fatigue, work overload, and sleepiness on a sample of commercial airline pilots, presented by Alaminos-Torres and others in 2023 [53].

Recently, various studies were conducted regarding fatigue-risk issues related to flight operations. In order to detect and reduce fatigue risks in flight operations, various measurement and analysis methods have been presented in the last decade.

In 2014, Borghini and others reviewed the neurophysiological measurements in pilots/drivers during their operational tasks, with the objective of summarizing the main neurophysiological findings regarding the measurements of the pilot/driver’s brain activity during their performance and its connection with the mental workload, mental fatigue, or situational awareness [54]. In 2015, Thomas and others collected physiological and performance data from commercial flight crews performing simulated operations under both rested and fatigued conditions in order to evaluate the effects of varying levels of fatigue and workload on pilot performance and physiological responses and constructed a statistical/machine learning model that was able to accurately categorize fatigue-related data for each individual pilot [55]. In 2018, Lee and Kim proposed a fatigue model for airline pilots, which verified that pilot physical fatigue, mental decline, and rest defects are affected by seven factors: flight direction, crew scheduling, partnership, aircraft environment, job assignment, ethnic difference, and hotel environment [56]. In 2020, Hu and Lodewijks explored effective non-invasive methods and psychophysiological indicators for detecting and monitoring fatigue in car drivers and aircraft pilots [57]. Papanikou and others studied the neuroscientific methodology able to yield markers of subtle pilot states, such as drowsiness and microsleep episodes, that can be integrated into a decision support system for operational aviation settings [58]. Coombes and others gathered and presented data on reported rates of occurrence and predicted fatigue risk exposure associated with UK airline pilot work schedules [59]. In 2021, Qin and others studied approaches for mental fatigue detection based on psychophysiological measurements in flying-relevant environments by performing an experiment where several conventional heart rate variability and ocular indices were examined to study their relevance to mental fatigue [60]. In 2022, Bongo and Seva studied the effect of fatigue in air traffic controllers’ workload, situational awareness, and control strategy by performing a case study in an actual tower control center in the Philippines, using questionnaires based on situational awareness methods, the Samn-Perelli fatigue scale, and the visual attention self-report [61]. In 2023, Sun and others used a software model as an analysis tool for pilot’s fatigue risk prediction, as well as the fatigue self-assessment scale and the objective alertness test, to conduct a comprehensive analysis and an assessment of the fatigue risk of flight crews before and after the COVID-19 epidemic [62]. Hamann and Carstengerdes performed an experiment where mental fatigue was induced during a simulated flight task, and data were collected from the participants using concurrent electroencephalography (EEG)—functional Near-Infrared Spectroscopy (fNIRS) assessment methods, and the performance and self-reports, with the aim of determining valid physiological assessment measures [63]. Veksler and others integrated a biomathematical fatigue model with a task network model in order to estimate the pilot performance degradation and to provide real-time information on pilot fatigue and the expected performance on specific aircraft operations [64].

In order to find correlations among various sets of workload settings indicators, causal modeling techniques and methods can be used. These methods use datasets of collected data and build causal models that show correlations among them. Using causal models, specifically by detecting the correlations (impacts among variables), it can be learned which variables should be modified to obtain the desired performance of targeted indicator(s), in this case flight crew fatigue. Simulating workload settings can reveal significant useful information regarding fatigue risk mitigation.