Stress and Workload Assessment in Aviation-A Narrative Review

Can humanoid robots be used as a cognitive offloading tool?

Cognitive load occurs when the demands of a task surpass the available processing capacity, straining mental resources and potentially impairing performance efficiency, such as increasing the number of errors in a task. Owing to its ubiquity in real-world scenarios, the existence of offloading strategies to reduce cognitive load is not new to experts and nonexperts, and many of these strategies involve technology (e.g., using Calendar Apps to remember scheduled events). Surprisingly, little is known about the potential use of humanoid robots for cognitive offloading. We will examine studies assessing the influence of humanoid robots on cognitive tasks requiring the resolution of cognitive conflict to determine whether their presence facilitates or hinders cognitive performance. Our analysis focuses on standardized cognitive conflict paradigms, as these effectively simulate real-life conflict scenarios (i.e., everyday challenges in focusing on the task and ignoring distractions). In these studies, robots were involved by either participating in the tasks, providing social cues, or observing human performance. By identifying contexts where humanoid robots support cognitive offloading and where they may undermine it, this work contributes to a deeper understanding of cognitive processes in human-robot interaction (HRI) and informs the design of interventions aimed at improving task performance and well-being in professional HRI settings.

Detection of Cognitive Load Modulation by EDA and HRV

Electrodermal activity (EDA) and heart rate variability (HRV) offer opportunities to grasp critical manifestations of the nervous autonomic system using low-intrusive sensing tools. A key question relies on the capacity to adequately process EDA and HRV signals to extract cognitive load markers, a multifaceted construct with intricate neural networks functioning, where emotions interfere with cognition. Here, 34 participants (20 males, 19.2 ± 1.3 years) were exposed to two-back mental tasking and watching emotionally charged images while recording EDA and HRV. HRV signals were processed using variable frequency complex demodulation (VFCDM) and wavelet packet transform (WPT) to provide high- and low-frequency (HF and LF) markers. Three methods were used to extract EDA indices: VFCDM (EDATVSYMP), WPT (EDAWPT), and convex-optimization (EDACVX). Cognitive load and emotion epochs were distinguished by significant differences in NASA-TLX scores, mental fatigue, and stress, on the one hand; and by EDACVX and, remarkably, EDATVSYMP and HF-HRVVFCDM on the other hand. A linear mixed-effects model and stepwise backward selection procedure showed that these two markers were main predictors of the NASA-TLX score (cognitive load). The individual perception of cognitive load was finally discriminated by k-means clustering, showing three profiles of autonomic responses relying, respectively, on EDATVSYMP, HF-HRVVFCDM, or a mix of these two markers. The existence of EDA-, HRV-, and EDA/HRV-derived profiles might explain why previous attempts that have predominantly employed a single biosignal often remained unconclusive in evaluating the perceived cognitive load, thereby demonstrating the added value of the present approach to monitor mental-related workload in human operators.

A study of dynamic functional connectivity changes in flight trainees based on a triple network model

The time-varying functional connectivity of the Central Executive Network (CEN), Default Mode Network (DMN), and Salience Network (SN) in flight trainees during a resting state was investigated using dynamic functional network connectivity (dFNC). The study included 39 flight trainees and 37 age- and sex-matched healthy controls. Resting-state fMRI data and behavioral test outcomes were obtained from both groups. Independent component analysis (ICA), sliding window, and K-means clustering approaches were utilized for evaluating functional network connectivity (FNC) and temporal metrics based on the triple networks. Correlation analyses were performed on the behavioral assessments and these metrics. The flight trainees demonstrated a significantly enhanced functional connection linking the CEN and DMN in state 2 (P < 0.05, FDR corrected). Additionally, flight trainees spent less time in state 5, while they exhibited a protracted mean dwell time and fractional windows in state 2, which were significantly correlated with accuracy on the Berg Card Sorting Test (BCST) and Change Detection Test (all P < 0.05). The improved connectivity of flight trainees between the CEN and DMN following the completion of rigorous flight training resulted in increased stability. This enhancement may be relevant to cognitive abilities such as decision-making, memory, and information integration. When multitasking, flight trainees displayed superior visual processing skills and enhanced cognitive flexibility. dFNC research provides a unique perspective on the sophisticated cognitive capabilities that are required in high-demand, high-stress occupations such as piloting, thereby providing significant insights into the intricate brain mechanisms that are inherent in these domains.

Correlations of pilot trainees' brainwave dynamics with subjective performance evaluations: insights from EEG microstate analysis

Objective

This study aims to investigate the relationship between the subjective performance evaluations on pilot trainees' aircraft control abilities and their brainwave dynamics reflected in the results from EEG microstate analysis. Specifically, we seek to identify correlations between distinct microstate patterns and each dimension included in the subjective flight control evaluations, shedding light on the neurophysiological mechanisms underlying aviation expertise and possible directions for future improvements in pilot training.

Background

Proficiency in aircraft control is crucial for aviation safety and modern aviation where pilots need to maneuver aircraft through an array of situations, ranging from routine takeoffs and landings to complex weather conditions and emergencies. However, the neurophysiological aspects of aviation expertise remain largely unexplored. This research bridges the gap by examining the relationship between pilot trainees' specific brainwave patterns and their subjective evaluations of flight control levels, offering insights into the cognitive underpinnings of pilot skill efficiency and development.

Method

EEG microstate analysis was employed to examine the brainwave dynamics of pilot trainees while they performed aircraft control tasks under a flight simulator-based pilot training process. Trainees' control performance was evaluated by experienced instructors across five dimensions and their EEG data were analyzed to investigate the associations between the parameters of specific microstates with successful aircraft control.

Results

The experimental results revealed significant associations between aircraft control levels and the parameters of distinct EEG microstates. Notably, these associations varied across control dimensions, highlighting the multifaceted nature of control proficiency. Noteworthy correlations included positive correlations between microstate class E and class G with aircraft control, emphasizing the role of attentional processes, perceptual integration, working memory, cognitive flexibility, decision-making, and executive control in aviation expertise. Conversely, negative correlations between microstate class C and class F with aircraft control indicated links between pilot trainees' cognitive control and their control performance on flight tasks.

Conclusion

The findings underscore the multidimensional nature of aircraft control proficiency and emphasize the significance of attentional and cognitive processes in achieving aviation expertise. These neurophysiological markers offer a basis for designing targeted pilot training programs and interventions to enhance trainees' aircraft control skills.

In the captain's chair: a cross-sectional study on back pain among commercial airline pilots in Saudi Arabia

BACKGROUND

Musculoskeletal disorders, including back pain, pose a significant challenge to workforce health, particularly in professions characterized by prolonged periods of sedentary activities. This challenge is notably relevant in commercial airline piloting due to unique ergonomic issues. Despite extensive research on back pain in various occupational settings, an understanding of the specific factors contributing to back pain among commercial airline pilots in Saudi Arabia is still lacking.

METHODS

This cross-sectional survey aimed to investigate the prevalence of back pain among Saudi Arabian commercial airline pilots. A structured questionnaire, developed through literature review and expert consultation, covered demographic information, occupational details, and back pain history. The survey was administered online to active pilots recruited through the Saudi Airlines Medical Services, with data collection spanning six weeks.

RESULTS

Among 310 predominantly male participants (99.0%), a significant prevalence of back pain was identified, with 71.3% reporting lower back pain in the last 12 months. Factors associated with a decreased likelihood of low back pain included comfortable seat conditions (odds ratio [OR]: 0.3; 95% confidence interval [CI]: 0.2-0.7), a senior officer position (OR: 0.5, 95% CI: 0.3-0.8), and regular exercise (OR: 0.6, 95% CI: 0.3-1.0). Higher flying hours in the past year were associated with an increased prevalence of back pain (OR: 2.2, 95% CI: 1.2-4.1). The multivariable analysis revealed that a comfortable seat was the single independent factor most significantly associated with back pain (OR: 0.3; 95% CI: 0.1-0.7).

CONCLUSIONS

This study highlights a notable prevalence of back pain among Saudi Arabian commercial airline pilots, underscoring the need for targeted interventions. The critical role of seat comfort emphasizes the importance of ergonomic considerations. Findings contribute to the global discourse on pilot health, emphasizing the necessity for ongoing evaluation and potential revisions to existing guidelines.

CLINICAL TRIAL NUMBER

Not applicable.

Workload of home care nurses: Italian adaptation, validity, and reliability of the National Aeronautics and Space Administration Task Load Index questionnaire

The aim of this study was to adapt the National Aeronautics and Space Administration Task Load Index (NASA-TLX) to the home care setting and translate and validate it in Italian. An online questionnaire containing the Italian version of the NASA-TLX adapted to the home care setting was administered to home care nurses to measure workload. Content Validity Index, Exploratory, and Confirmatory Factor Analyses were used to measure the psychometric characteristics of the modified NASA-TLX. The modified Italian version of NASA-TLX_HC-IT showed good psychometric characteristics in measuring the workload of home care nurses, with excellent fit indices. The reliability, calculated with Cronbach's alpha, was 0.73, indicating adequate reliability. A negative correlation between workload and job satisfaction among home care nurses, as well as a positive association between high workload and intention to leave the workplace, was verified. The modified Italian version of the NASA-TLX_HC-IT was confirmed to be a valid and reliable instrument to measure workload in home care nursing. Furthermore, the correlation between workload and the intention to leave the workplace among home care nurses was an important result that community nursing managers should consider preventing the shortage of home care nurses.

Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments

While individuals fail to assess their mental health subjectively in their day-to-day activities, the recent development of consumer-grade wearable devices has enormous potential to monitor daily workload objectively by acquiring physiological signals. Therefore, this work collected consumer-grade physiological signals from twenty-four participants, following a four-hour cognitive load elicitation paradigm with self-chosen tasks in uncontrolled environments and a four-hour mental workload elicitation paradigm in a controlled environment. The recorded dataset of approximately 315 hours consists of electroencephalography, acceleration, electrodermal activity, and photoplethysmogram data balanced across low and high load levels. Participants performed office-like tasks in the controlled environment (mental arithmetic, Stroop, N-Back, and Sudoku) with two defined difficulty levels and in the uncontrolled environments (mainly researching, programming, and writing emails). Each task label was provided by participants using two 5-point Likert scales of mental workload and stress and the pairwise NASA-TLX questionnaire. This data is suitable for developing real-time mental health assessment methods, conducting research on signal processing techniques for challenging environments, and developing personal cognitive load assistants.

Short Rest Between Flights Is Associated With Increased Serum Stress Hormone Levels in Airline Pilots: A Cross-Sectional Study

BACKGROUND

Work-related stress is a critical area of research in civil aviation, given the potential for severe consequences when airline pilots (APs) are overwhelmed or unable to perform optimally. While pilots are traditionally considered to be exposed to various stressors, the impact of specific occupational characteristics on stress in the aviation industry remains inadequately understood. Considering that biomarkers are increasingly being utilized as objective measures of stress in human research, this cross-sectional study investigated the association between occupational variables and serum levels of cortisol and dehydroepiandrosterone sulfate (DHEAS) as stress biomarkers in commercial APs.

METHODS

A total of 120 male APs completed a survey assessing the following work-related characteristics: position (captain vs. first officer), years of experience, total flight hours, flight hours in the preceding year, inter-flight rest duration, and flight route length. Serum cortisol and DHEAS concentrations were determined from venous blood samples obtained between 08:00 and 09:00 a.m., following a minimum eight-hour fasting period. The biomarker data were analyzed in relation to the aforementioned occupational characteristics.

RESULTS

The mean serum cortisol and DHEAS concentrations in the entire cohort were 8.5±2.1 µg/dL and 214.6±96.4 µg/dL, respectively. There were no significant differences in the levels of the two stress hormones in relation to position, years of experience, total flight hours, flight hours in the preceding year, or flight route length. However, an inter-flight rest period of less than one hour was significantly associated with elevated serum cortisol (P<0.01) and reduced DHEAS levels (P<0.001) compared to longer rest durations. Notably, a stepwise decrease in DHEAS concentrations was observed for rest periods of <1 hour, 1-4 hours, 4-24 hours, and >24 hours. After adjustment for potential confounders in multivariable analyses, a rest period of <1 hour remained independently associated with both serum cortisol (odds ratio [OR]=1.09, 95% confidence interval [CI]=1.04-1.13, P<0.01) and DHEAS (OR=0.94, 95% CI=0.92-0.97, P<0.001).

CONCLUSIONS

Serum stress hormones in APs are associated with short inter-flight rest periods. Optimization of rest durations may contribute to improved pilot well-being and performance. Further research is warranted to determine ideal rest period lengths and develop interventions to mitigate the potential adverse effects of abbreviated rest periods between flights.

Reducing flight upset risk and startle response: A study of the wake vortex alert with licensed commercial pilots

The study aimed at investigating the impact of an innovative Wake Vortex Alert (WVA) avionics on pilots' operation and mental states, intending to improve aviation safety by mitigating the risks associated with wake vortex encounters (WVEs). Wake vortices, generated by jet aircraft, pose a significant hazard to trailing or crossing aircrafts. Despite existing separation rules, incidents involving WVEs continue to occur, especially affecting smaller aircrafts like business jets, resulting in aircraft upsets and occasional cabin injuries. To address these challenges, the study focused on developing and validating an alert system that can be presented to air traffic controllers, enabling them to warn flight crews. This empowers the flight crews to either avoid the wake vortex or secure the cabin to prevent injuries. The research employed a multidimensional approach including an analysis of human performance and human factors (HF) issues to determine the potential impact of the alert on pilots' roles, tasks, and mental states. It also utilizes Human Assurance Levels (HALs) to evaluate the necessary human factors support based on the safety criticality of the new system. Realistic flight simulations were conducted to collect data of pilots' behavioural, subjective and neurophysiological responses during WVEs. The data allowed for an objective evaluation of the WVA impact on pilots' operation, behaviour and mental states (mental workload, stress levels and arousal). In particular, the results highlighted the effectiveness of the alert system in facilitating pilots' preparation, awareness and crew resource management (CRM). The results also highlighted the importance of avionics able to enhance aviation safety and reducing risks associated with wake vortex encounters. In particular, we demonstrated how providing timely information and improving situational awareness, the WVA will minimize the occurrence of WVEs and contribute to safer aviation operations.

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.

Coping with Examination Stress: An Emotion Analysis

Stress is an important factor affecting human behavior, with recent works in the literature distinguishing it as either productive or destructive. The present study investigated how the primary emotion of stress is correlated with engagement, focus, interest, excitement, and relaxation during university students' examination processes. Given that examinations are highly stressful processes, twenty-six postgraduate students participated in a four-phase experiment (rest, written examination, oral examination, and rest) conducted at the International Hellenic University (IHU) using a modified Trier protocol. Network analysis with a focus on centralities was employed for data processing. The results highlight the important role of stress in the examination process; correlate stress with other emotions, such as interest, engagement, enthusiasm, relaxation, and concentration; and, finally, suggest ways to control and creatively utilize stress.

Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.

Robotic surgery and work-related stress: A systematic review

Despite robot-assisted surgery (RAS) becoming increasingly common, little is known about the impact of the underlying work organization on the stress levels of members of the operating room (OR) team. To this end, assessing whether RAS may impact work-related stress, identifying associated stress factors and surveying relevant measurement methods seems critical. Using three databases (Scopus, Medline, Google Scholar), a systematic review was conducted leading to the analysis of 20 articles. Results regarding OR team stress levels and measurement methods were heterogeneous, which could be explained by differing research conditions (i.e., lab. vs. real-life). Relevant stressors such as (in)experience with RAS and quality of team communication were identified. Development of a common, more reliable methodology of stress assessment is required. Research should focus on real-life conditions in order to develop valid and actionable knowledge. Surgical teams would greatly benefit from discussing RAS-related stressors and developing team-specific strategies to handle them.

Cerebral oxygenation and perfusion kinetics monitoring of military aircrew at high G using novel fNIRS wearable system

Introduction

Real-time physiological episode (PE) detection and management in aircrew operating high-performance aircraft (HPA) is crucial for the US Military. This paper addresses the unique challenges posed by high acceleration (G-force) in HPA aircrew and explores the potential of a novel wearable functional near-infrared spectroscopy (fNIRS) system, named NIRSense Aerie, to continuously monitor cerebral oxygenation during high G-force exposure.

Methods

The NIRSense Aerie system is a flight-optimized, wearable fNIRS device designed to monitor tissue oxygenation 13-20 mm below the skin's surface. The system includes an optical frontend adhered to the forehead, an electronics module behind the earcup of aircrew helmets, and a custom adhesive for secure attachment. The fNIRS optical layout incorporates near-distance, middle-distance, and far-distance infrared emitters, a photodetector, and an accelerometer for motion measurements. Data processing involves the modified Beer-Lambert law for computing relative chromophore concentration changes. A human evaluation of the NIRSense Aerie was conducted on six subjects exposed to G-forces up to +9 Gz in an Aerospace Environmental Protection Laboratory centrifuge. fNIRS data, pulse oximetry, and electrocardiography (HR) were collected to analyze cerebral and superficial tissue oxygenation kinetics during G-loading and recovery.

Results

The NIRSense Aerie successfully captured cerebral deoxygenation responses during high G-force exposure, demonstrating its potential for continuous monitoring in challenging operational environments. Pulse oximetry was compromised during G-loading, emphasizing the system's advantage in uninterrupted cerebrovascular monitoring. Significant changes in oxygenation metrics were observed across G-loading levels, with distinct responses in Deoxy-Hb and Oxy-Hb concentrations. HR increased during G-loading, reflecting physiological stress and the anti-G straining maneuver.

Discussion

The NIRSense Aerie shows promise for real-time monitoring of aircrew physiological responses during high G-force exposure. Despite challenges, the system provides valuable insights into cerebral oxygenation kinetics. Future developments aim for miniaturization and optimization for enhanced aircrew comfort and wearability. This technology has potential for improving anti-G straining maneuver learning and retention through real-time cerebral oxygenation feedback during centrifuge training.

Should the existing science of teams be applied to fluid teams? An exploration of fluid team effectiveness within the context of healthcare simulation

Introduction

Fluid teams have become increasingly prevalent and necessary for modern-day issues, yet they differ from more traditional teams, on which much of the current teams literature is based. For example, fluid teams are often comprised of members from different disciplines or organizational divisions who do not have a shared history or future, as they come together to perform a critical, time-sensitive task, and then disband. For these reasons, the mechanisms through which they function and perform may differ from those of more traditional teams, and research is needed to better understand these differences.

Methods

To this end, this study utilized critical incident techniques and thematic analysis to examine fluid teams within healthcare, one of the primary contexts in which they are prevalent. Interdisciplinary faculty and students in the medical field who encounter fluid teams within simulation-based education were prompted to reflect on key factors that facilitate or hinder fluid team effectiveness.

Results

Primary themes extracted pertained to the conditions fluid teams operate within (e.g., high-stress), the behaviors and emergent states that contribute to their success (e.g., communication), and the KSAO's of value for members of fluid teams to possess (e.g., readiness). These themes were then compared to existing literature, yielding the identification of some similarities but also many important differences between fluid and traditional teams.

Discussion

A series of practical recommendations for how to promote fluid team effectiveness is then presented.

Correction: Masi et al. Stress and Workload Assessment in Aviation-A Narrative Review. Sensors 2023, 23, 3556

The published publication [...].

EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing

Objective.To date, most research on electroencephalography (EEG)-based mental workload detection for passive brain-computer interface (pBCI) applications has focused on identifying the overall level of cognitive resources required, such as whether the workload is high or low. We propose, however, that being able to determine the specific type of cognitive resources being used, such as visual or auditory, would also be useful. This would enable the pBCI to take more appropriate action to reduce the overall level of cognitive demand on the user. For example, if a high level of workload was detected and it is determined that the user is primarily engaged in visual information processing, then the pBCI could cause some information to be presented aurally instead. In our previous work we showed that EEG could be used to differentiate visual from auditory processing tasks when the level of processing is high, but the two modalities could not be distinguished when the level of cognitive processing demand was very low. The current study aims to build on this work and move toward the overall objective of developing a pBCI that is capable of predicting both the level and the type of cognitive resources being used.Approach.Fifteen individuals undertook carefully designed visual and auditory tasks while their EEG data was being recorded. In this study, we incorporated a more diverse range of sensory processing conditions including not only single-modality conditions (i.e. those requiring one of either visual or auditory processing) as in our previous study, but also dual-modality conditions (i.e. those requiring both visual and auditory processing) and no-task/baseline conditions (i.e. when the individual is not engaged in either visual or auditory processing).Main results.Using regularized linear discriminant analysis within a hierarchical classification algorithm, the overall cognitive demand was predicted with an accuracy of more than 86%, while the presence or absence of visual and auditory sensory processing were each predicted with an accuracy of approximately 70%.Significance.The findings support the feasibility of establishing a pBCI that can determine both the level and type of attentional resources required by the user at any given moment. This pBCI could assist in enhancing safety in hazardous jobs by triggering the most effective and efficient adaptation strategies when high workload conditions are detected.

Skin biophysical parameters and serum dermokine levels in airline pilots: a comparative study with office workers

Introduction

Concerns are growing in the aviation industry about occupational skin diseases like malignant melanoma (MM) among airline pilots (APs), due to the unique working environment that exposes them to various skin stressors.

Aim

To compare five skin biophysical parameters in a group of 40 male APs, each matched in terms of age and service tenure (minimum of 5 years) with a control group of 40 male office workers (OWs). Considering the potential role of dermokine (DMKN) in skin barrier dysfunction and the pathogenesis of MM, we further analyzed the serum levels of this molecule and correlated them with the measured skin parameters.

Material and methods

Stratum corneum skin hydration, transepidermal water loss (TEWL), sebum content, erythema index (EI), and melanin index (MI) were quantified by non-invasive instruments in the cheek region. Serum DMKN levels were measured using a commercially available enzyme-linked immunosorbent assay kit.

Results

Compared with OWs, the skin of APs exhibited a decrease in hydration levels in the stratum corneum, coinciding with a higher TEWL. However, there was no significant variance in sebum content between the groups. MI was notably higher in APs than in OWs, as was EI. In APs, serum DMKN levels were independently associated with MI (β = 0.56, p < 0.05).

Conclusions

We found a significant link between the profession of an airline pilot and changes in skin biophysical parameters. Further research into the interplay between serum DMKN levels and the risk of MM in APs is warranted.

Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

Frustrated Total Internal Reflection Measurement System for Pilot Inceptor Grip Pressure

Sensing the interaction between the pilot and the control inceptors can provide important information about the pilot's activity during flight, potentially enabling the objective measurement of the pilot workload, the application of preventive actions against loss of situational awareness, and the identification of the insurgence of adverse couplings with the vehicle dynamics. This work presents an innovative pressure-sensing device developed to be seamlessly integrated into the grips of conventional aircraft control inceptors. The sensor, based on frustrated total internal reflection of light, is composed of low-cost elements and can be easily manufactured to be applicable to different hand pressure ranges. The characteristics of the sensor are first demonstrated in laboratory calibration tests. Subsequently, applications in flight simulator testing are presented, focusing on the objective representation of the pilot's instantaneous workload.