Stress resilience is recognized as an important occupational prerequisite for air traffic controllers (ATCs). A system for input/output multimodal stress resilience assessment based on physiological ...features has been developed and applied in the ATC selection process on 40 ATC candidates, as well as on 40 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- and electrodermal activity-based acoustic startle response features, like startle reactivity and startle habituation, and acoustic startle modulation-related features, like fear-potentiated startle, prepulse inhibition of the startle response, and discrimination of startle responses in danger versus safety experimental conditions. Variability of each feature is assessed and illustrated in 8-D physiological resilience space. Statistically significant differences (p < 0.05) between the two groups have been obtained for the three most relevant of eight 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.
In order to improve intelligent Human-Computer Interaction it is important to create a personalized adaptive emotion estimator that is able to learn over time emotional response idiosyncrasies of ...individual person and thus enhance estimation accuracy. This paper, with the aim of identifying preferable methods for such a concept, presents an experiment-based comparative study of seven feature reduction and seven machine learning methods commonly used for emotion estimation based on physiological signals. The analysis was performed on data obtained in an emotion elicitation experiment involving 14 participants. Specific discrete emotions were targeted with stimuli from the International Affective Picture System database. The experiment was necessary to achieve the uniformity in the various aspects of emotion elicitation, data processing, feature calculation, self-reporting procedures and estimation evaluation, in order to avoid inconsistency problems that arise when results from studies that use different emotion-related databases are mutually compared. The results of the performed experiment indicate that the combination of a multilayer perceptron (MLP) with sequential floating forward selection (SFFS) exhibited the highest accuracy in discrete emotion classification based on physiological features calculated from ECG, respiration, skin conductance and skin temperature. Using leave-one-session-out crossvalidation method, 60.3% accuracy in classification of 5 discrete emotions (sadness, disgust, fear, happiness and neutral) was obtained. In order to identify which methods may be the most suitable for real-time estimator adaptation, execution and learning times of emotion estimators were also comparatively analyzed. Based on this analysis, preferred feature reduction method for real-time estimator adaptation was minimum redundancy – maximum relevance (mRMR), which was the fastest approach in terms of combined execution and learning time, as well as the second best in accuracy, after SFFS. In combination with mRMR, highest accuracies were achieved by k-nearest neighbor (kNN) and MLP with negligible difference (50.33% versus 50.54%); however, mRMR+kNN is preferable option for real-time estimator adaptation due to considerably lower combined execution and learning time of kNN versus MLP.
•We compared accuracy and learning/execution times of emotion estimation methods.•Feature selection methods had more impact on accuracy than machine learning methods.•Combination of SFFS and MLP methods exhibited the highest emotion estimation accuracy.•mRMR+kNN combination is preferable for real-time adaptation of emotion estimation.•Skin conductance features contributed the most to the emotion estimation accuracy.
Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause ...massive stress-related disorders. Motivated by the ongoing global coronavirus pandemic, the article provides an overview of scientific evidence regarding adverse impact of diverse human disasters on mental health in afflicted groups and societies. Following this broader context, psychosocial impact of COVID-19 as a specific global human disaster is presented, with an emphasis on disturbing mental health aspects of the ongoing pandemic. Limited resources of mental health services in a number of countries around the world are illustrated, which will be further stretched by the forthcoming increase in demand for mental health services due to the global COVID-19 pandemic. Mental health challenges are particularly important for the Republic of Croatia in the current situation, due to disturbing stress of the 2020 Zagreb earthquake and the high pre-pandemic prevalence of chronic Homeland-War-related posttraumatic stress disorders. Comprehensive approach based on digital psychiatry is proposed to address the lack of access to psychiatric services, which includes artificial intelligence, telepsychiatry and an array of new technologies, like internet-based computer-aided mental health tools and services. These tools and means should be utilized as an important part of the whole package of measures to mitigate negative mental health effects of the global coronavirus pandemic. Our scientific and engineering experiences in the design and development of digital tools and means in mitigation of stress-related disorders and assessment of stress resilience are presented. Croatian initiative on enhancement of interdisciplinary research of psychiatrists, psychologists and computer scientists on the national and EU level is important in addressing pressing mental health concerns related to the ongoing pandemic and similar human disasters.
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 article, the fNIRS and ECG signals were simultaneously collected from 32 participants and used to classify three levels of cognitive load on <inline-formula> <tex-math notation="LaTeX">{n} </tex-math></inline-formula>-back task. A set of 30 fNIRS and ECG features proposed in this article 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 article 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.
Affective multimedia documents such as images, sounds or videos elicit emotional responses in exposed human subjects. These stimuli are stored in affective multimedia databases and successfully used ...for a wide variety of research in psychology and neuroscience in areas related to attention and emotion processing. Although important all affective multimedia databases have numerous deficiencies which impair their applicability. These problems, which are brought forward in the paper, result in low recall and precision of multimedia stimuli retrieval which makes creating emotion elicitation procedures difficult and labor-intensive. To address these issues a new core ontology STIMONT is introduced. The STIMONT is written in OWL-DL formalism and extends W3C EmotionML format with an expressive and formal representation of affective concepts, high-level semantics, stimuli document metadata and the elicited physiology. The advantages of ontology in description of affective multimedia stimuli are demonstrated in a document retrieval experiment and compared against contemporary keyword-based querying methods. Also, a software tool Intelligent Stimulus Generator for retrieval of affective multimedia and construction of stimuli sequences is presented.
Acute and chronic neck pain are common medical conditions, and the treatment typically includes physical therapy involving daily exercises. Insufficient motivation of people afflicted with neck pain ...to adhere to the prescribed exercise regimen may delay their recovery. Accordingly, in this work, we propose a system that motivates the users to perform neck exercises by engaging them in a serious exergame within virtual reality (VR) environment. The system measures the users’ neck movements via a few static and dynamic kinematic tests and a novel VR serious game, tailored to the neck range of motion of each individual user. The game is designed to make the users perform rehabilitative neck movements according to the prescribed exercise regimen while playing. The analysis of acquired data from VR hardware provides insight into flexibility of the neck during head movements and overall neck kinematics, which is valuable for assessment of pain-related stiffness, as well as for progress monitoring. In a user study performed with the proposed system and the Oculus Rift DK2 VR headset, we show that the users find exercising more interesting and engaging when using the proposed system, and that introducing visually rich VR environments makes the users more motivated to continue exercising.
The significant proportion of severe psychological problems related to intensive stress in recent large peacekeeping operations underscores the importance of effective methods for strengthening the ...prevention and treatment of stress-related disorders. Adaptive control of virtual reality (VR) stimulation presented in this work, based on estimation of the person's emotional state from physiological signals, may enhance existing stress inoculation training (SIT). Physiology-driven adaptive VR stimulation can tailor the progress of stressful stimuli delivery to the physiological characteristics of each individual, which is indicated for improvement in stress resistance. Following an overview of physiology-driven adaptive VR stimulation, its major functional subsystems are described in more detail. A specific algorithm of stimuli delivery applicable to SIT is outlined.
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.
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