LC-MS-Based Urine Metabolomics for the Identification of Biomarkers Related to The Occurrence and Severity of Flight Fatigue

Background: Aircrew fatigue is a major contributor of operational errors in civil and military aviation, which translates a comprehensive performance associated with both neuromuscular and cognitive states. Here we have used untargeted and non-invasive urinary metabolomics to explore flight fatigue-related aberrations. In this sense, we aimed to identify biomarkers that could better monitor pilot fatigue and also assess its severity to prevent ‘nonfunctional over-reached’ state, thus promoting flight safety. Methods: In this study, 22 active-duty male pilots, who conducted different flight hour duties, were recruited to mimic different levels of fatigue. For this, respective urine samples were collected, before and after flight, and analyzed by liquid chromatography/mass spectrometry (LC/MS). Results: Except for the fatty acids and some amino acids, significant changes on metabolite levels were observed during the progression of flight fatigue. Most of these metabolites corresponded to acyl carnitines, carbohydrates, purines and indoles. The majority of amino acids were downregulated after the flight mission. A total of 61 metabolites were found to be significantly changed along with the extent of flight fatigue. To efficiently discriminate the occurrence of flight fatigue, three candidate biomarkers (beta-guanidinopropionic acid, 3-dehydro-L-gulonate and 2-propylpent-3-enoic acid) were further characterized. Lastly, Bayes discriminant function models were established to stratify pilots with severity of fatigue and, therefore, to aid in flight risk management.Conclusion: To our knowledge, this study inaugurally provides a metabolic profiling in response to flight fatigue, thus offering a novel and effective way to monitor and manage this physiological condition.

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

0210 Evaluating the Sensitivity of Sleep Measures for Monitoring Pilot Fatigue in Operational Settings

Introduction Previously, combined data analyses of four pilot fatigue monitoring studies including 237 pilots flying long-haul and ultra-long range (ULR) flights found no association between pilots’ actigraphic sleep in flight and psychomotor vigilance task (PVT) performance at top-of-descent (TOD; beginning of the landing phase of flight). The present study aimed to determine whether measures of in-flight sleep recorded with polysomnography (PSG) are more sensitive predictors of pilots’ PVT performance near TOD than actigraphic measures. Methods Data were re-analysed from 41 Singapore Airlines A340-500 pilots (median age 47, range 29–58 years) monitored on a ULR trip between Singapore and Los Angeles (average flight duration outbound = 15.6 hrs; inbound = 17.2 hrs). In-flight sleep was recorded simultaneously with PSG (scored in 30-second epochs) and actigraphy (recorded in 30-second epochs and scored in conjunction with sleep diary information). PSG- and actigraphy-determined time awake were calculated as the duration between the end of the last epoch scored as sleep (PSG) or software-scored sleep interval (actigraphy) and the start time of the 10-minute PVT completed near TOD. Results Linear mixed modelling indicated that after controlling for flight sector and intra- and inter-individual variability, neither PSG-derived total in-flight sleep (F (1, 44.4) = 0.006, p= 0.941) and time awake (F (1, 34.3) = 0.431, p= 0.516), nor actigraphic total in-flight sleep (F (1, 51.1) = 0.161, p= 0.69) and time awake (F (1, 34.9) = 0.23, p= 0.634) were associated with PVT response speed at TOD. Conclusion In this context, actigraphy produced identical findings to polysomnography and remains a valid alternative for monitoring in-flight sleep of groups of pilots during ULR flights. Further research is needed to determine whether PVT performance is a discriminatory measure of fatigue-related impairment in pilots. Support This analysis was supported by the Massey University College of Health Research Fund. The Singapore Airlines study was funded by the Singapore Civil Aviation Authority. We thank Dr Jarnail Singh for permission to use these data.

Analyzing pilots’ fatigue for prolonged flight missions: Multimodal analysis approach using vigilance test and eye tracking

Performance decrement associated with pilot fatigue is considered a leading contributor to aviation accidents and fatalities. The output of prevalent pilot fatigue methodologies (both subjective & objective) either suffer from human judgement bias or require complex data processing. Moreover, studies catering to long duration flight missions have not been performed. Presently, we investigate the impact of fatigue on pilot performance for long duration of a flight mission composed of multiple take-offs and landings. We propose a new multimodal approach that integrates traditional fatigue metrics with eye tracking methodology. The effect of fatigue on the pilots’ eye movements was evaluated using information theory-based entropy measures. Results showed an increase in the fatigue level (measured by mean reaction times and the number of lapses) with increase in flight duration. The entropy measures showed that visual attention distribution and scanning strategy both became random in nature as fatigue level increased in pilots. Obtained results suggest fatigue decreases both information searching and processing capability in pilots. The proposed method can show which aspect of the pilot performance becomes impaired by fatigue and thus can be applied to evaluate fatigue onset in real time, which enables timely recovery interventions.