Stress resilience is recognized as an important occupational prerequisite for Air Traffic Controllers (ATC). A system for input/output multimodal stress resilience assessment based on physiological features has been developed and applied in the ATC selection process on forty ATC candidates, as well as on forty age/sex-matched control subjects. The input stimulation paradigm includes: acoustic startle stimuli and their prepulse and fear-potentiated modulations, airblasts, and semantically relevant aversive images and sounds. The output physiological features include: resting heart rate variability and respiratory sinus arrhythmia, cardiac allostasis, electromyogram (EMG)-and electrodermal activity (EDA)-based acoustic startle response features, like startle reactivity and startle habituation, as well as acoustic startle modulation-related features, like fear-potentiated startle, prepulse inhibition of the startle response, and discrimination of startle responses in danger vs. safety experimental conditions. Variability of each feature is assessed and illustrated in 8-dimensional physiological resilience space. Statistically significant differences (p<0.05) between the two groups have been obtained for the three most relevant of 8 applied features; specifically, ATC candidates exhibited significantly higher resting respiratory sinus arrhythmia, lower startle reactivity, and more pronounced cardiac allostasis than the control group. The observed feature variability justifies future research efforts toward augmenting the traditional ATC selection process with the presented stress resilience assessment approach. The proposed research paradigm can be also applied in selection processes of similarly stressful occupations such as first responders, airline/military pilots, military personnel, among others.

Cognitive load can be estimated using individuals’ task performance, their subjective measures, and neurophysiological measures. Neurophysiological measures, which among others include brain activation signals obtained with various brain imaging techniques, such as the functional near-infrared spectroscopy (fNIRS), and signals from the peripheral physiology, such as the electrocardiography (ECG) signal, allow an objective and continuous estimation of cognitive load. In this paper, the fNIRS and ECG signals were simultaneously collected from 32 participants and used to classify three levels of cognitive load on n-back task. A set of 30 fNIRS and ECG features proposed in this paper enables the classification of different levels of cognitive load on n-back task using the support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA) classification models. When combining the fNIRS and ECG features, three difficulties of the n-back task were classified with the mean accuracies ranging from 61% to 67%, while two difficulties were classified with the mean accuracy ranging from 70% to 84%. The most important features in the classification are discussed. The results presented in this paper extend the existing empirical evidence that combining brain imaging and peripheral physiology features increases the accuracy of multi-level cognitive load classification, thus further underscoring the importance of multimodal approach to cognitive load classification.

This article provides an overview of the mental health challenges faced by pilots and air traffic controllers (ATCs), whose stressful professional lives may negatively impact global flight safety and security. The adverse effects of mental health disorders on their flight performance pose a particular safety risk, especially in sudden unexpected startle situations. Therefore, the early detection, prediction and prevention of mental health deterioration in pilots and ATCs, particularly among those at high risk, are crucial to minimize potential air crash incidents caused by human factors. Recent research in artificial intelligence (AI) demonstrates the potential of machine and deep learning, edge and cloud computing, virtual reality and wearable multimodal physiological sensors for monitoring and predicting mental health disorders. Longitudinal monitoring and analysis of pilots’ and ATCs physiological, cognitive and behavioral states could help predict individuals at risk of undisclosed or emerging mental health disorders. Utilizing AI tools and methodologies to identify and select these individuals for preventive mental health training and interventions could be a promising and effective approach to preventing potential air crash accidents attributed to human factors and related mental health problems. Based on these insights, the article advocates for the design of a multidisciplinary mental healthcare ecosystem in modern aviation using AI tools and technologies, to foster more efficient and effective mental health management, thereby enhancing flight safety and security standards. This proposed ecosystem requires the collaboration of multidisciplinary experts, including psychologists, neuroscientists, physiologists, psychiatrists, etc. to address these challenges in modern aviation.

Comprehensive multimodal psychophysiological measurements and smart data analysis based on wearable and low-cost technologies could enhance traditional air traffic controller (ATC) selection process. Many recent studies in neuro-cognitive science and stress resilience illustrated effectiveness of these multimodal measurements and appropriate metrics in comprehensive assessment of ATCs’ mental states, such as cognitive workload, cognitive decline, attention deficit, fatigue, emotional and behavioural problems, etc. Accordingly, this article is focused on innovation efforts in ATC selection protocols based on a set of comprehensive stimuli and corresponding multimodal psychophysiological measurements. The concept of enhancement of ATC selection process presented in this article includes complex physiological, oculometric and speech measurements and appropriate metrics. From these multimodal measurements during specific stimulation tasks, which include different versions of acoustic startle stimuli, airblasts, semantically relevant aversive images and sounds, different versions of Stroop tests, visual tracking test, a complex set of multimodal-multidimensional features is computed as predictors of ATC candidates’ future performance, like: stress resilience, workload capacity, attention, visual performance, working memory etc. Such cost-effective, more objective, non-invasive preliminary measurements, lasting no longer than 45 minutes may have good discriminative power and might be used in ATC selection processes as enhancement of current selection procedures. Comprehensive analysis of presented multimodal features during different experimental conditions might also be very useful in selection processes of other stressful professional jobs, like first responders, pilots, astronauts etc.

In this paper we investigate the potential of generic physiological features of stress resilience in predicting air traffic control (ATC) candidates' performance in a highly-stressful low-fidelity ATC simulator scenario. Stress resilience is highlighted as an important occupational factor that influences the performance and well-being of air traffic control officers (ATCO). Poor stress management, besides the lack of skills, can be a direct cause of poor performance under stress, both in the selection process of ATCOs and later in the workplace. 40 ATC candidates, within the final stages of their selection process, underwent a stimulation paradigm for elicitation and assessment of various generic task-unrelated physiological features, related to resting heart rate variability (HRV) and respiratory sinus arrhythmia (RSA), acoustic startle response (ASR) and the physiological allostatic response, which are all recognized as relevant psychophysiological markers of stress resilience. The multimodal approach included analysis of electrocardiography, electromyography, electrodermal activity and respiration. We make advances in computational methodology for assessment of physiological features of stress resilience, and investigate the predictive power of the obtained feature space in a binary classification problem: prediction of high- vs. low-performance on the developed ATC simulator. Our novel approach yields a relatively high 78.16% classification accuracy. These results are discussed in the context of prior work, while considering study limitations and proposing directions for future work.