Technical approaches for measurement of human errors

Human error is a significant contributing factor in a very high proportion of civil transport, general aviation, and rotorcraft accidents. The technical details of a variety of proven approaches for the measurement of human errors in the context of the national airspace system are presented. Unobtrusive measurements suitable for cockpit operations and procedures in part of full mission simulation are emphasized. Procedure, system performance, and human operator centered measurements are discussed as they apply to the manual control, communication, supervisory, and monitoring tasks which are relevant to aviation operations

Miscellaneous EEG preprocessing and machine learning for pilots' mental states classification: implications

Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. These findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs

Brain-wave measures of workload in advanced cockpits: The transition of technology from laboratory to cockpit simulator, phase 2

The present Phase 2 small business innovation research study was designed to address issues related to scalp-recorded event-related potential (ERP) indices of mental workload and to transition this technology from the laboratory to cockpit simulator environments for use as a systems engineering tool. The project involved five main tasks: (1) Two laboratory studies confirmed the generality of the ERP indices of workload obtained in the Phase 1 study and revealed two additional ERP components related to workload. (2) A task analysis' of flight scenarios and pilot tasks in the Advanced Concepts Flight Simulator (ACFS) defined cockpit events (i.e., displays, messages, alarms) that would be expected to elicit ERPs related to workload. (3) Software was developed to support ERP data analysis. An existing ARD-proprietary package of ERP data analysis routines was upgraded, new graphics routines were developed to enhance interactive data analysis, and routines were developed to compare alternative single-trial analysis techniques using simulated ERP data. (4) Working in conjunction with NASA Langley research scientists and simulator engineers, preparations were made for an ACFS validation study of ERP measures of workload. (5) A design specification was developed for a general purpose, computerized, workload assessment system that can function in simulators such as the ACFS

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 153)

This bibliography lists 175 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1976

A comprehensive analysis of machine learning and deep learning models for identifying pilots’ mental states from imbalanced physiological data

This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential

USING IMAGERY PRACTICE TO IMPROVE AIRLINE PILOT SITUATIONAL AWARENESS

Pilot error remains the primary cause of airline airplane accidents (Federal Aviation Administration, n.d.). Airline pilots have relied on Crew Resource Management and Threat Error Management to reduce or eliminate errors (Helmreich & Foushee, 2019). Unfortunately, the worldwide accident rate continues to increase (International Air Transport Association, 2021), demonstrating the need for further research into improving aviation safety. Current regulations do not require imagery training for airline pilots to improve situational awareness (Federal Aviation Administration, 2017a). Athletes and other professionals, such as musicians and medical professionals, use imagery to improve performance (Munzert et al., 2009). Imagery practice may improve the situational awareness of airline pilots. This study examined the relationship between imagery practice and airline pilot situational awareness. The researcher used an experimental posttest design with a group of airline pilots that received imagery training and a practice period. The data analysis answered the research questions and objectives using data provided by the participants who completed an interactive video survey. The researcher compared the survey results with airline pilots without imagery practice, measuring Endsley\u27s (1995) three levels of situational awareness, including perception, comprehension, and projection. The study\u27s results produced three findings that emphasize the effects of the research. Pilots who practiced imagery more often had higher levels of situational awareness during the video survey than pilots who practiced less. Although there was an improvement in the group that practiced imaging a flight, further research may improve the effectiveness of imagery practice. More experienced pilots participated in the study compared to less experienced pilots. Further research regarding safety training experience and situational awareness could add to the findings of this study, along with Wang et al. (2021) findings regarding pilots using personal attributes such as emotional intelligence that replace inadequate training to maintain situational awareness

Multimodal Analysis of Pilot’s Fatigue During a Multi-Phase Flight Mission

One troubling threat to successful flight missions is attributed to fatigue induced and errors. Therefore, discovering effective methods to assess fatigue has been a major topic discussed by professional pilots and aviation experts. Fatigue is a major human factor related issue in aviation and currently subject to increased discussion by aviation administrations and professional pilots. Therefore, effective assessment of fatigue will provide opportunities to reduce the risk of fatigue-induced errors. Currently available subjective measures that assess fatigue can be somewhat affected by external and internal factors, that might cause biased judgment. Therefore, Psychomotor Vigilance Task (PVT), which provides objective measures, can be a viable approach to measure fatigue. In addition, eye movement analysis might augment the fatigue assessment, because eye movement analysis is an unobtrusive approach that does not require direct contact with the participant and can be measured for a long duration. However, it is unknown how eye movement characteristics are correlated with fatigue. In this research, a multi-modal fatigue measurement framework was developed by combining the PVT analysis with eye movement analysis. In detail, PVT measures (i.e., reaction time, lapses & false starts) and eye movement characteristics (i.e., eye fixation duration, pupil size, number of eye fixations, gaze entropy) were measured to determine pilots’ fatigue level under different flight conditions. The results show that significant correlations exist among the eye movement characteristics and the PVTs measures. The proposed multi-modal approach show promise on evaluating pilot fatigue in near real time, which in turn might enable timely recovery interventions

Adaptive Allocation of Decision Making Responsibility Between Human and Computer in Multi-Task Situations

A unified formulation of computer-aided, multi-task, decision making is presented. Strategy for the allocation of decision making responsibility between human and computer is developed. The plans of a flight management systems are studied. A model based on the queueing theory was implemented

Методика формування дій екіпажу при відмовах в системах авіоніки

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник дипломної роботи: к.т.н., доцент кафедри авіоніки Грищенко Юрій ВіталійовичEstablished, that necessary to determine the amplitude of change of angle of attack. Analyzed oscillograms of real flights, construct a histogram of the distribution of the roll angle flight without failures after the 3rd turn. Thesis materials are recommended for using alarming system in case of training of pilots’ anti-stress program on simulators. Foreseeable assumptions about the development of the research object: increasing the quality of the flight operation, reliability and aviation safety in general, by training pilots on simulators to withstand the stress that arises in emergencies

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 367)

This bibliography lists 205 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System during Aug. 1992. Subject coverage includes the following: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance