Assessment of Ocular and Physiological Metrics to Discriminate Flight Phases in Real Light Aircraft

Objective: The purpose of the present study was to find psychophysiological proxies that are straightforward to use and could be implemented in actual flight conditions to accurately discriminate pilots’ workload levels. Background: Piloting an aircraft is a complex activity where cognitive limitations may jeopardize flight safety. There is a need to implement solutions to monitor pilots’ workload level to improve flight safety. There has been recent interest in combining psychophysiological measurements. Most of these studies were conducted in flight simulators at the group level, limiting the interpretation of the results. Methods: We conducted an experiment with 11 pilots performing two standard traffic patterns in a light aircraft. Five metrics were derived from their ocular and cardiac activities and were evaluated through three flight phases: takeoff, downwind, and landing. Results: Statistical analyses showed that the saccadic rate was the most efficient metric to distinguish between the three flight phases. In addition, a classifier trained on the ocular data collected from the first run predicted the flight phase within a second run with an accuracy of 75%. No gain in the classifier accuracy has been found by combining cardiac and ocular metrics. Conclusions: Ocular-based metrics may be more suitable than cardiac ones to provide relevant information on pilots’ flying activity in operational settings. Applications: Electrocardiographic and eye-tracking devices could be implemented in future cockpits as additional flight data for accident analysis, an objective pilot’s state evaluation for training, and proxies for human-machine interactions to improve flight safety

Monitoring eye movements in real flight conditions for flight training purpose

Eye-tracking is a relevant technique to investigate the pilot’s cognitive performance. It can enhance pilots’ training and be used foron-line monitoring for smart cockpits. Most of the studies are conducted in flight simulators which may limit the interpretation of theeye-tracking data. In this study, we investigate the possibility to measure eye movement in real flight. We conducted an experiment with 7 pilots performing two traffic patterns and basic flight maneuvers in a real light aircraft. We analyzed the distribution of attentionover the main areas of interest (flight instruments) during the different flight phases. These data were confronted with operationalprocedures and discussed with flight instructors in terms of flight safety and recommendations for training. Also, a classifier trainedon the first traffic pattern could predict the three phases (take-off, downwind, and landing) of the second one with a mean accuracy of70

EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 165, March 1977

This bibliography lists 198 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1977

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979

Multimodal Neuroergonomic Approaches to Human Behavior and Cognitive Workload in Complex High-Risk Semantically Rich Environments: A Case Study of Local & En-Route Air Traffic Controllers

Fast-paced technology advancements have enabled us to create ecologically valid simulations of high risk, complex, and semantically rich environments in which human interaction and decision-making are the keys to increase system performance. These advances have improved our capabilities of exploring, quantifying, and measuring the underlying mechanisms that guide human behavior using sophisticated neuroergonomic devices; and in turn, improve human performance and reduce human errors. In this thesis, multimodal approaches consisted of a self-report analysis, eye-tracking analysis, and functional near-infrared spectroscopy analysis were used to investigate how veteran local & en-route air traffic controllers carry out their operational tasks. Furthermore, the correlations among the cognitive workload and physiological measures (i.e. eye movement characteristics and brain activities) were investigated. Combining the results of these experiments, we can observe that the multimodal approaches show promise on exploring the underlying mechanisms of workload and human interaction in a complex, high-risk, and semantically rich environment. This is because cognitive workload can be considered as a multidimensional construct and different devices or approaches might be more effective in sensing changes in either the task difficulty or complexity. The results can be used to find ways to better train the novices

A Psychophysiological Assessment of the Efficacy of Event-Related Potentials and Electroencephalogram for Adaptive Task Allocation

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocations decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a compensatory tracking task was switched between manual and automatic task modes based upon the participant\u27s EEG. ERPs were also gathered to an auditory, oddball task. Participants in the yoked condition performed the same tasks under the exact sequence of task allocations that participants in the experimental group experienced. The control condition consisted of a random sequence of task allocations that was representative of each participant in the experimental group condition. Therefore, the design allowed a test of whether the performance and workload benefits seen in previous studies using this biocybernetic system were due to adaptive aiding or merely to the increase in task mode allocations. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower tracking errors scores and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of their implications for adaptive automation design

Toward the real time estimation of the attentional state through ocular activity analysis

L'analyse d'incidents aéronautiques et d'expériences en laboratoire a montré que la tunnélisation attentionnelle amène les pilotes à négliger des alarmes critiques. Une piste intéressante pour répondre à ce problème s'appuie sur les systèmes adaptatifs qui pourraient assister l'opérateur en temps réel (en changeant le comportement du pilote automatique par exemple). Ce type de systèmes adaptatifs requiert l'état de l'opérateur en entrée. Pour cela, des méthodes d'inférence de l'état de l'opérateur doublées de métriques de la tunnélisation attentionnelle doivent être proposées. Le but de cette thèse de doctorat est d'apporter la preuve que la détection de la tunnélisation attentionnelle est possible en temps réel. Pour cela une méthode adaptative neuro-floue utilisant les métriques de la tunnélisation attentionnelle sera proposée, ainsi que de nouvelles métriques de la tunnélisation attentionnelle qui ne dépendent pas du contexte de l'opérateur, et qui sont calculables en temps réel. L'algorithme d'identification des états de l'oeil (ESIA) est proposé en ce sens. Les métriques attentionnelles en sont dérivées et testées dans le contexte d'une expérience robotique dont le design favorise la tunnélisation attentionnellle. Nous proposons également une nouvelle définition du ratio exploitation/exploration d'information dont la pertinence en tant que marqueur de la tunnélisation attentionnelle est démontrée statistiquement. Le travail est ensuite discuté et appliqué sur divers cas d'étude en aviation et robotique.The analysis of aerospace incidents and laboratory experiments have shown that attentional tunneling leads pilots to neglect critical alarms. One interesting avenue to deal with this issue is to consider adaptive systems that would help the operator in real time (for instance: switching the auto-pilot mode). Such adaptive systems require the operator's state as an input. Therefore, both attentional tunneling metrics and state inference techniques have to be proposed. The goal of the PhD Thesis is to provide attentional tunneling metrics that are real-time and context independent. The Eye State Identification Algorithm (ESIA) that analyses ocular activity is proposed. Metrics are then derived and tested on a robotic experiment meant for favouring attentional tunneling. We also propose a new definition of the explore/exploit ratio that was proven statistically to be a relevant attentional tunneling marker. This work is then discussed and applied to different case studies in aviation and robotics

Predicting Inattentional Blindness with Pupillary Response in a Simulated Flight Task

Inattentional blindness (IB) is the failure of observers to notice the presence of a clearly viewable but unexpected visual event when attentional resources are diverted elsewhere. Knowing when an operator is unable to respond or detect an unexpected event may help improve safety during task performance. Unfortunately, it is difficult to predict when such failures might occur. The current study was a secondary data analysis of data collected in the Human and Autonomous Vehicle Systems Laboratory at NASA Langley Research Center. Specifically, 60 subjects (29 male, with normal or corrected-to-normal vision, mean age of 34.5 years (SD = 13.3) were randomly assigned to one of three automation conditions (full automation, partial automation, and full manual) and took part in a simulated flight landing task. The dependent variable was the detection/non-detection of an IB occurrence (a truck on the landing runway). Scores on the NASA-TLX workload rating scale varied significantly by automation condition. The full automation condition reported the lowest subjective task load followed by partial automation and then manual condition. IB detection varied significantly across automation condition. The moderate workload condition of partial automation exhibited the lowest likelihood of IB occurrence. The low workload full automation condition did not differ significantly from the manual condition. Subjects who reported higher task demand had increased pupil dilation and subjects with larger pupil dilation were more likely to detect the runway incursion. These results show eye tracking may be used to identify periods of reduced unexpected visual stimulus detection for possible real-time IB mitigation