Free Example of Pilot Fatigue during Flight Essay

Modern commercial flight operations require flight crews to be adjusted to non-domicile time zones during Ultra Long Range Operations (ULR), which last more than sixteen hours of operational duties in-flight environment. These conditions pose significant challenges to the pilot’s decision-making and subsequently provoke circadian misalignment, impaired coordination, and disruption of perception. These physiological conditions affect the pilot’s performance, due to the generation of fatigue, as a state of tiredness, which does not synch with the biological and circadian rhythms of the individual’s body.

The innovative approaches to the operational and strategic management of fatigue were introduced through initiation of Fatigue Risk Management Systems (FRMS), Biomathematical Fatigue Modeling, and Operator Status Detection Technologies that require the pilot’s feedback for maintenance of the safe flight, and elimination of fatigue-related errors. Thereby, the paper will focus on the types of fatigue, fatigue-related degradations of the pilot’s performances, and non-prescriptive approaches, which address fatigue-related challenges associated with flight operations.

Literature Review

During flight operations, the pilot may experience short term occurrence of tiredness, which includes deceased ability to concentrate, reduced alertness and vigilance, increased reaction time, impaired judgment, and inconsistent performance, due to the loss of situational awareness. These factors are caused by sleep loss (physical fatigue), as an outcome from prolonged performance of cognitive work or persistent stress (mental fatigue), and as a result of tiredness, which can occur after a period of strenuous effort (acute fatigue). The latter includes skill fatigue that creates timing disruption of the pilot’s inconsistent performance, and the pilot’s perceptual disruption with concentrated attention on the objects and movements, which are located in the center of his vision.

In this respect, generalized and severe fatigue occurs due to sustained psychological stress, which accelerates glandular secretions, and requires reserve energy provision by a liver. Furthermore, circadian misalignment, also known as jet lags, occurs due to the abovementioned depletion of physical energy and provokes the pilot’s increased vulnerability to plan continuation error and subsequent brief disengagement from the flight environment.

Therefore, FRMS was initiated to quantify and address the problem of influence of underlying interaction of sleep and circadian physiology on the pilot’s performance levels.  Therefore, the evidence-based scheduling tools estimate equations that predict a fatigue risk metric and correlate it with examined sleep history, time of the day and workload. In this respect, Biomathematical Models of human fatigue focus on minimizing fatigue-related incidents and accidents during flight operations. Furthermore, these models predict the occurrence and severity of fatigue and deliver optimal countermeasures to the individual. Additionally, generated predictions indicate a probability or risks, which are associated with one of the real-world outcomes.

Therefore, Fatigue Management Technologies provide the pilot with non-prescriptive strategies of mitigation of fatigue-related challenges. In this respect, fatigue monitoring devices provide meaningful measures of the pilot’s alertness and performance abilities, based on his physiological behavior.

Methodology

The research is based on the secondary research methods, which presuppose re-examination of data on the fatigue-related factors, and suggestion of mitigation of the fatigue-causing conditions with technological and operational strategies.

In this respect, qualitative methods help to identify the sequence of the fatigue risk factors, their correlation with fatigue-causing conditions, which provoke the fatigue state of the individual. These methods also assist in identifying whether pilot fatigue alters his decision-making and performance and creates the risk of committing pilot errors that lead to serious incidents or accidents.

Quantitative methods help to identify that FRMS and Biomathematical Scheduling Tools are effective countermeasures of the fatigue-related challenges because these technologies monitor the pilot’s physiological behavior, and suggest meaningful measures of fighting the possibility of fatigue-related errors.

Data collection methods help to identify that Fatigue Management Technologies quantify the impact of the pilot’s physiological behavior on his performance levels through simulation of circadian alertness, assessment of fatigue audit, calculation of expected sleep times and conditions of chronic sleep restrictions. In this respect, data analysis methods helped determine which strategies the pilot should use, in order to mitigate real-world outcomes from the fatigue-related challenges.

Data Analysis

The cause-effect examination of fatigue-related challenges assists in identifying such factors as oxygen deficiency, jet lags, physical and psychological stresses and sleep deprivation that provoke the following negative changes of the pilot’s physiological behavior and physical characteristics, namely: a) cognitive narrowing of attention; b) failures in monitoring of the flight environment; c) impaired crew resource management practice through the pilot’s feelings of indifference to the operational environment; and d) loss of the individual’s initiative to effort (FAA, 2008; Rathjen et al., 2008). From the perspective of a crew member, fatigue-related factors reduce the pilot’s effectiveness of communication, degrade his response accuracy, and disengage him from the multi-pilot cockpit environment.

In this respect, the perspectives of the research are based on the knowledge of the fatigue-causing conditions and imply suggestion of effective fatigue monitoring devices, whose data collection and data processing help detect the physiological state of an individual for further mitigation of the fatigue-related challenges. Furthermore, the research considers the opportunities of deployment of the Fatigue Management Technologies in the cockpit of an aircraft, in order to provide an estimation of alertness levels and the pilot’s performance ability in real-time. Additionally, the results of the research suggest the effectiveness of the Operator Status Detection Technologies and Biomathematical Modeling of human fatigue-causing conditions.

Results

Given the understanding of the impact of underlying interaction of sleep and circadian physiology of the pilot, the research findings suggest that Biomathematical Scheduling Tools can be considered as effective, evidence-based Fatigue Management Technologies, regarding their employment within the existing Safety Management System. Furthermore, it should be pointed out that, combination of these prescriptive and non-prescriptive approaches toward mitigation and management of fatigue-related challenges will help minimize fatigue-related incidents and accidents during flight operations.

In this respect, one of the following Fatigue Detection Technologies can be employed in the multi-pilot cockpit environment, namely: a) Circadian Alertness Simulator, which examines the correlation between fatigue indexes and accidents rates through incorporation of the pilot’s sleep history; b) Fatigue Audit InterDyne (FAID), which estimates fatigue-related score, considering the time of sleep that the pilot has; c) Interactive Neurobehavioral Model (INN), which predicts circadian phase shift, considering differences of the pilot’s exposure to light; d) Sleep, Activity, Fatigue and Task Effectiveness (SAFNE), which calculates the pilot’s time spent in the sleep, based on the indexes, provided by the installed sleep reservoir, estimated circadian rhythm, and measured sleep inertia component; e) Sleep/Wake Predictor (SWP), which calculates chronic-related conditions of sleep restriction. 

Furthermore, it was researched that, the on-line operator status technologies help monitor the pilot’s impaired physiological behavior and physical characteristics and determine his alertness levels and performance ability, considering the indexes of event-related brain potentials, eye gaze, facial feature recognition, muscle tone, head position, percent eye closure and wrist inactivity (Caldwell et al., 2009). These technologies include event-brain potential’s estimation with the help of EEG devices, detection of micro nodes and microsleeps of the pilot by means of XYZ devices, estimation of the individual’s attention level by the Co-Pilot techniques; the pilot’s sleep diagnostics with the help of Optalert devices, and wrist-alertness estimation by means of PERCLOS techniques. Additionally, it should be pointed out that, such devices provide the video segments of the calculations and assign the pilot for the countermeasures of reduction of the fatigue-related conditions. The strategic countermeasures are generalized in the following section.

Discussion

Considering evidence-based pattern of correlation of fatigue risk factors and fatigue-causing conditions, and previous experience of fatigue-related pilot errors, the research findings justify that approaches of FRMS and Biomathematical Modeling should be coordinated within the Operator Status Detection Technologies. This coordination will help address the problem of aviation operations and fatigue challenges associated with flight environment. Furthermore, apart from operational management of fatigue, such coordinated approach will help develop strategic actions of pilot fatigue management that he should perform during flight and duty periods in the multi-pilot cockpit environment.

The abovementioned actions include a) engagement in alternating periods of activity and relaxation during flight; b) social interactions with other crew members; c) reduction of prolonged cognitive work through organization and delegation of tasks; d) maintenance of alertness through practicing of arm and leg stretching; e) regular consumption of water and food for the provision of energy, and prevention of the onset of fatigue; and f) engagement of pre-planned naps and moves about the cabin if regulation and airline policies permit.

In addition, it is suggested that, individual status detection technologies can be deployed to increase the pilot’s awareness and effective feedback to the estimated event-related conditions within the fatigue-related risks of pilot errors.

Conclusion

Pilot fatigue is often associated with the risk of committing the task error and the results from the adverse fatigue-related factors and fatigue-causing conditions. Qualitative methods of the research helped identify that oxygen deficiency, depletion of physical energy, jet legs and ULR are the most common causes of the fatigue-related challenges in commercial aviation operations. In this respect, data collection methods help to identify that these factors and conditions alert sufficient decision-making and effective performance of the pilot through his impaired physiological behavior and degraded physical characteristics in the flight environment.

For that purpose, the abovementioned methods justify deployment of the evidence-based Biomathematical Scheduling Tools that quantify the impact of interaction of sleep and circadian physiology on the pilot’s performance levels, and provide meaningful measures for minimization of fatigue-related incidents and accidents during flight operations.  

Furthermore, quantitative methods justify the coordination of fatigue management technologies and fatigue monitoring technologies that provide the pilot with the strategic and operational countermeasures of the fatigue-related challenges. In this respect, data analysis methods help to determine specificity of fatigue risk factors, their effect on the pilot’s decision-making and performance and suggest the installation of fatigue monitoring devices that detect the pilot’s physiological behavior, his physical characteristics and flight environment conditions that provoke the risks of committing the task error.

The findings of the research show that FRMS can be employed within existing Safety Management System, and should be coordinated with the Biomathematical Scheduling approach, which presupposes deployment of either of the fatigue detection models, such as CAS, FAID, INN, SAFNE or SWP. Furthermore, previous cognitive experience of the fatigue-caused flight environment and evidence-based protocols of the fatigue monitoring devices suggest advisable countermeasures that the pilot can perform, in order to mitigate the adverse effects of fatigue-related challenges. 

In addition, the research findings suggest further investigation of effectiveness of the Operator Status Detection Technologies for the pilot’s self-assessment estimation and sufficient feedback to the advised meaningful measures of these devices. Additionally, such monitoring technologies provide real time measures of the pilot’s alertness levels and performance abilities and include calculated indexes of his physiological behavior and physical characteristics.