Aerospace medicine and biology: A continuing bibliography with indexes (supplement 335)

This bibliography lists 143 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during March, 1990. 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

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 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

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents

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

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Évaluation de la charge mentale des pilotes en manœuvre aérienne

La charge de travail cognitive d'un pilote d'aviation, qui englobe sa capacité mentale à effectuer les manœuvres d'un avion, varie selon l’étape de pilotage et le nombre de tâches convergeant simultanément sur le pilote. Cette charge de travail peut entraîner des erreurs de pilotage aux conséquences graves. La plupart des erreurs se produisent pendant la procédure de décollage ou d'atterrissage. Cette étude vise à mesurer et prédire la charge de travail cognitive d'un pilote lors d'une procédure de décollage afin de mieux comprendre et potentiellement prévenir ces erreurs humaines. Pour y parvenir, nous avons créé une solution logicielle pour mesurer et surveiller en temps réel la fréquence cardiaque, la dilatation pupillaire et la charge de travail cognitive d'un pilote. Le logiciel est également capable de déclencher des événements de défaillance pour déclencher une modification de la charge de travail cognitive à la demande. À l'aide d'un casque électroencéphalogramme (EEG) permettant de mesurer l’activité électrique du cerveau, d'un moniteur de fréquence cardiaque, d'un eye tracker et d'un simulateur, nous avons créé une configuration d'environnement où les pilotes devaient faire décoller un avion A320 avec et sans pannes sans le savoir au préalable. Cette étude a rassemblé 136 décollages sur 13 pilotes pour plus de 9 heures de données de séries chronologiques, soit 2 millions de lignes combinées. De plus, nous avons étudié la relation entre la fréquence cardiaque, la dilatation de la pupille et la charge de travail cognitive lors d'une tâche critique. Cette étude a révélé, à l'aide d'une analyse statistique, qu'un moment critique, comme une panne de moteur, augmente la fréquence cardiaque, la dilatation de la pupille et la charge de travail cognitive d'un pilote. Ensuite, cette recherche a utilisé différents modèles d'apprentissage automatique et d'apprentissage en profondeur pour prédire la charge de travail cognitive d'un pilote pendant le décollage. Nous avons constaté qu'en utilisant un modèle d'apprentissage en profondeur long short-term memory empilé, nous étions en mesure de prédire la charge de travail cognitive 5 secondes dans le futur. Le modèle long short-term memory empilé a donné une erreur quadratique moyenne (MSE) de 44,09, une erreur racine quadratique moyenne (RMSE) de 6,64 et une erreur absolue moyenne de 5,28, prouvant qu'il est possible de prédire la charge de travail cognitive.The cognitive workload for an aviation pilot, which englobes a pilot's mental capacity to perform aircraft's maneuvers, varies according to the piloting stage and the number of tasks converging simultaneously on the pilot. This workload can lead to piloting errors with severe consequences, where most errors occur during the takeoff or landing procedure. This study aims to predict the cognitive workload of a pilot during a takeoff procedure in order to better understand and potentially prevent these human errors. To achieve this, we created a software solution to measure and monitor in real-time the heart rate, pupil dilation, and cognitive workload of a pilot. The software is also capable of triggering failure events to trigger a change in cognitive workload on demand. Using an electroencephalogram (EEG) headset which measures the electrical brain activity, a heart rate monitor, an eye tracker, and a simulator, we created an environment setup where pilots had to take off an A320 airplane with and without failures without priorly knowing it. This study gathered 136 takeoffs across 13 pilots for more than 9 hours of time-series data, or 2 million rows combined. Moreover, we investigated the relation between heart rate, pupil dilation, and cognitive workload during a critical task. This study found, using statistical F-test analysis, that a critical moment, such as an engine failure, augments the heart rate, pupil dilation, and cognitive workload of a pilot. Next, this research utilized different machine learning and deep learning models to predict the cognitive workload of a pilot during takeoff. We found that, when using a stacked long short-term memory deep learning model, we were able to predict future cognitive workload 5 seconds into the future. The stacked long short-term memory model resulted in a mean square error of 44.09, a root mean square error of 6.64, and an mean absolute error of 5.28, demonstrating that it is possible to predict future cognitive workload

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is propose

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 341)

This bibliography lists 133 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September 1990. 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

Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 345)

This publication is a cumulative index to the abstracts contained in Supplements 333 through 344 of Aerospace Medicine and Biology: A Continuing Bibliography. Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number, and accession number