650 research outputs found

    Aerospace Medicine and Biology: A continuing bibliography, supplement 191

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    A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

    Technical approaches for measurement of human errors

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    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

    Aerospace medicine and biology: A continuing bibliography with indexes (supplement 327)

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    This bibliography lists 127 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during August, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

    Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning

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    This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases

    Advancing aviation safety through machine learning and psychophysiological data: a systematic review

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    In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior

    Multimodal approach for pilot mental state detection based on EEG

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    The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA’s open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach

    Aerospace medicine and biology: A continuing bibliography with indexes (supplement 368)

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    This bibliography lists 305 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System during Sep. 1992. The subject coverage concentrates on the biological, physiological, psychological, and environmental effects to which humans are subjected during and following simulated or actual flight in the Earth's atmosphere or in interplanetary space. References describing similar effects on biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. Applied research receives the most emphasis, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion

    Proceedings of the Seventeenth Annual Conference on Manual Control

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    Manual control is considered, with concentration on perceptive/cognitive man-machine interaction and interface

    Mental-State Estimation, 1987

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    Reports on the measurement and evaluation of the physiological and mental state of operators are presented
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