Prediction of Task Performance From Physiological Features of Stress Resilience

Enhancing Aviation Safety through AI-Driven Mental Health Management for Pilots and Air Traffic Controllers

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.

Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System

OBJECTIVE

The employment of wearable systems for continuous monitoring of vital signs is increasing. However, due to substantial susceptibility of conventional bio-signals recorded by wearable systems to motion artifacts, estimation of the respiratory rate (RR) during physical activities is a challenging task. Alternatively, functional Near-Infrared Spectroscopy (fNIRS) can be used, which has been proven less vulnerable to the subject's movements. This paper proposes a fusion-based method for estimating RR during bicycling from fNIRS signals recorded by a wearable system.

METHODS

Firstly, five respiratory modulations are extracted, based on amplitude, frequency, and intensity of the oxygenated hemoglobin concentration (O2Hb) signal. Secondly, the dominant frequency of each modulation is computed using the fast Fourier transform. Finally, dominant frequencies of all modulations are fused, based on averaging, to estimate RR. The performance of the proposed method was validated on 22 young healthy subjects, whose respiratory and fNIRS signals were simultaneously recorded during a bicycling task, and compared against a zero delay Fourier domain band-pass filter.

RESULTS

The comparison between results obtained by the proposed method and band-pass filtering indicated the superiority of the former, with a lower mean absolute error (3.66 vs. 11.06 breaths per minute, p<0.05). The proposed fusion strategy also outperformed RR estimations based on the analysis of individual modulation.

SIGNIFICANCE

This study orients towards the practical limitations of traditional bio-signals for RR estimation during physical activities.

Differences Between Good, Moderate and Poor Performers of a Verbal Fluency Task under Blue Light Exposure: An SPA-fNIRS Study

Individuals have different performance levels for cognitive tasks. Are these performance levels reflected in physiological parameters? The aim of this study was to address this question by systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). We aimed to investigate whether different verbal fluency task (VFT) performances under blue light exposure were associated with different changes in cerebrovascular oxygenation and systemic physiological activity. The VFT performance of 32 healthy subjects (17 female, 15 male, age: 25.5 ± 4.3 years) was investigated under blue light exposure (120 lux). The VFT, which contained letter and category fluency tasks, lasted 9 min. There were rest periods without light exposure before and after the VFT for 8 min and 15 min, respectively. Based on their number of correct responses, subjects were classified into three groups, i.e., good, moderate, and poor performers. During the entire experiment, we simultaneously measured changes in cerebral and systemic physiological parameters using the SPA-fNIRS approach. We found that the better the subject's performance was, the smaller the task-evoked changes in cerebrovascular hemodynamics and oxygenation in the prefrontal cortex. Performance-dependent changes were also evident for skin conductance, arterial oxygen saturation and mean arterial pressure. This is the first VFT study that applies the comprehensive SPA-fNIRS approach to determine the relationship between task performance and changes in cerebral oxygenation and systemic physiology. Our study shows that these parameters are indeed related and the performance is reflected in the task-evoked cerebrovascular and systemic physiological changes.

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients

The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.

Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach

This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate, peripheral temperature, and skin conductance. We measured the data with a biofeedback device from 71 individuals subjected to a 10-min psychophysiological stress test. The data exploration revealed that features' variability among test phases could be observed in a two-dimensional space with Principal Components Analysis (PCA). In this work, we demonstrate that the values of each feature within a phase are well organized in clusters. The new index we propose, Resilience to Stress Index (RSI), is based on this observation. To compute the index, we used non-supervised machine learning methods to calculate the inter-cluster distances, specifically using the following four methods: Euclidean Distance of PCA, Mahalanobis Distance, Cluster Validity Index Distance, and Euclidean Distance of Kernel PCA. While there was no statistically significant difference (p>0.01) among the methods, we recommend using Mahalanobis, since this method provides higher monotonic association with the Resilience in Mexicans (RESI-M) scale. Results are encouraging since we demonstrated that the computation of a reliable RSI is possible. To validate the new index, we undertook two tasks: a comparison of the RSI against the RESI-M, and a Spearman correlation between phases one and five to determine if the behavior is resilient or not. The computation of the RSI of an individual has a broader scope in mind, and it is to understand and to support mental health. The benefits of having a metric that measures resilience to stress are multiple; for instance, to the extent that individuals can track their resilience to stress, they can improve their everyday life.

Resilience, preparedness, and distress among family caregivers of patients with advanced cancer

OBJECTIVE

Resilience has been proposed as a primary factor in how many family caregivers of patients with advanced cancer are able to resist psychological strain and perform effectively in the role while bearing a high load of caregiving tasks. To evaluate this hypothesis, we examined whether self-perceived resilience is associated with distress (anxiety and depressive symptoms), caregiver preparedness, and readiness for surrogate decision-making among a racially diverse sample of family caregivers of patients with newly diagnosed advanced cancer.

METHODS

Secondary analysis of baseline data from two small-scale, pilot clinical trials that both recruited family caregivers of patients with newly diagnosed advanced cancer. Using multivariable linear regression, we analyzed relationships of resilience as a predictor of mood, caregiving preparedness, and readiness for surrogate decision-making, controlling for sociodemographics.

RESULTS

Caregiver participants (N = 112) were mean 56 years of age and mostly female (76%), the patient's spouse/partner (52%), and White (56%) or African-American/Black (43%). After controlling for demographics, standardized results indicated that higher resilience was relevantly associated with higher caregiver preparedness (beta = .46, p < .001), higher readiness for surrogate decision-making (beta = .20, p < .05) and lower anxiety (beta =  - .19, p < .05), and depressive symptoms (beta =  - .20, p < .05).

CONCLUSIONS

These results suggest that resilience may be critical to caregivers' abilities to manage stress, be effective sources of support to patients, and feel ready to make future medical decisions on behalf of patients. Future work should explore and clinicians should consider whether resilience can be enhanced in cancer caregivers to optimize their well-being and ability to perform in the caregiving and surrogate decision-making roles.

Assessment of Resilience of the Hellenic Navy Seals by Electrodermal Activity during Cognitive Tasks

Stress resilience plays a key role in task performance during emergencies, especially in occupations like military special forces, with a routine consisting of unexpected events. Nevertheless, reliable and applicable measurements of resilience in predicting task performance in stressful conditions are still researched. This study aimed to explore the stress response in the Hellenic Navy SEALs (HN-SEALs), using a cognitive–physiological approach. Eighteen candidates under intense preparation for their enlistment in the HN-SEALs and 16 healthy controls (HCs) underwent Stroop tests, along with mental-state and personality examination. Simultaneously, electrodermal activity (EDA) was assessed during each one of cognitive testing procedures. Compared to healthy control values, multiple components of EDA values were found decreased (p < 0.05) in the HN-SEALs group. These results were associated with an increase in resilience level in the HN-SEALs group, since a restricted sympathetic reactivity according to the reduced EDA values was observed during the stressful cognitive testing. This is the first report providing physiological measurements of the sympathetic response of HN-SEALs to a stressful situation and suggests that EDA turns out to be a simple and objective tool of sympathetic activation and it may be used as a complementary index of resilience in HN-SEALs candidates.