A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers

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.

Physiological measures of operators' mental state in supervisory process control tasks: a scoping review

Physiological measures are often used to assess the mental state of human operators in supervisory process control tasks. However, the diversity of research approaches creates a heterogeneous landscape of empirical evidence. To map existing evidence and provide guidance to researchers and practitioners, this paper systematically reviews 109 empirical studies that report relationships between peripheral nervous system measures and mental state dimensions (e.g. mental workload, mental fatigue, stress, and vigilance) of interest. Ocular and electrocardiac measures were the most prominent measures across application fields. Most studies sought to validate such measures for reliable assessments of cognitive task demands and time on task, with measures of pupil size receiving the most empirical support. In comparison, less research examined the utility of physiological measures in predicting human task performance. This approach is discussed as an opportunity to focus on operators' individual response to cognitive task demands and to advance the state of research.

Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3

The capability of measuring specific neurophysiological and autonomic parameters plays a crucial role in the objective evaluation of a human's mental and emotional states. These human aspects are commonly known in the scientific literature to be involved in a wide range of processes, such as stress and arousal. These aspects represent a relevant factor especially in real and operational environments. Neurophysiological autonomic parameters, such as Electrodermal Activity (EDA) and Photoplethysmographic data (PPG), have been usually investigated through research-graded devices, therefore resulting in a high degree of invasiveness, which could negatively interfere with the monitored user's activity. For such a reason, in the last decade, recent consumer-grade wearable devices, usually designed for fitness-tracking purposes, are receiving increasing attention from the scientific community, and are characterized by a higher comfort, ease of use and, therefore, by a higher compatibility with daily-life environments. The present preliminary study was aimed at assessing the reliability of a consumer wearable device, i.e., the Fitbit Sense, with respect to a research-graded wearable, i.e., the Empatica E4 wristband, and a laboratory device, i.e., the Shimmer GSR3+. EDA and PPG data were collected among 12 participants while they performed multiple resting conditions. The results demonstrated that the EDA- and PPG-derived features computed through the wearable and research devices were positively and significantly correlated, while the reliability of the consumer device was significantly lower.

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces

Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.

Real-time Pilot Crew's Mental Workload and Arousal Assessment During Simulated Flights for Training Evaluation: a Case Study

Training assessment is usually done by evaluating information derived from instructor's supervision related to the pilot's operational performance and behavior. However, this approach lacks objective measures, especially regarding the pilots' mental states while accomplishing the flight training tasks. The study therefore aimed at developing and testing a method for gathering and analyzing in real-time pilots' brain activity and skin conductance to improve the training evaluation. In this regard, Novice pilots' neurophysiological signals were acquired throughout multi-crew training sessions. The results demonstrated how the methodology proposed was able to endow real-time pilots' mental workload and arousal assessment for i) better evaluating training progress and operational behavior during the training session, and ii) for objectively comparing different training sessions.

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving the human–machine interaction system is to improve its understanding of the pilots’ status, including fatigue, stress, workload, etc. Monitoring pilots’ status can effectively prevent human error and achieve optimal human–machine collaboration. As such, there is a need to recognize pilots’ status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot’s behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots’ behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way.

EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports).

Air Force Pilot Expertise Assessment with Regard to Mental Effort Requested during Unusual Attitude Recovery Flight Training Simulations

Pilot training and expertise are key aspects in aviation. A traditional way of evaluating pilot expertise is to measure performance output. However, this approach provides a narrow view of the pilot’s capacity, especially with regard to mental and emotional profile. The aim of this study is hence to investigate whether neurophysiological data can be employed as an additional objective measure to assess the expertise of pilots. In this regard, it has been demonstrated that mental effort can be used as an indirect measure of operator expertise and capacity. An increase in mental effort, for instance, can automatically result in a decrease in the remaining capacity of the operator. To better investigate this aspect, we ask two groups of Italian Air Force pilots, experienced (Experts) and unexperienced (Novices), to undergo unusual attitude recovery flight training simulations. Their behavioral (unusual attitude recovery time), subjective (mental effort demand perception) and neurophysiological data (Electroencephalogram, EEG; Electrocardiogram, ECG) are collected during the entire flight simulations. Although the two groups do not exhibit differences in terms of unusual attitude recovery time and mental effort demand perception, the EEG-based mental effort index shows how Novices request significantly higher mental effort during unusual conditions.

Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving

Driver’s stress affects decision-making and the probability of risk occurrence, and it is therefore a key factor in road safety. This suggests the need for continuous stress monitoring. This work aims at validating a stress neurophysiological measure—a Neurometric—for out-of-the-lab use obtained from lightweight EEG relying on two wet sensors, in real-time, and without calibration. The Neurometric was tested during a multitasking experiment and validated with a realistic driving simulator. Twenty subjects participated in the experiment, and the resulting stress Neurometric was compared with the Random Forest (RF) model, calibrated by using EEG features and both intra-subject and cross-task approaches. The Neurometric was also compared with a measure based on skin conductance level (SCL), representing one of the physiological parameters investigated in the literature mostly correlated with stress variations. We found that during both multitasking and realistic driving experiments, the Neurometric was able to discriminate between low and high levels of stress with an average Area Under Curve (AUC) value higher than 0.9. Furthermore, the stress Neurometric showed higher AUC and stability than both the SCL measure and the RF calibrated with a cross-task approach. In conclusion, the Neurometric proposed in this work proved to be suitable for out-of-the-lab monitoring of stress levels.

Cognitive Workload of Tugboat Captains in Realistic Scenarios: Adaptive Spatial Filtering for Transfer Between Conditions

Changing and often class-dependent non-stationarities of signals are a big challenge in the transfer of common findings in cognitive workload estimation using Electroencephalography (EEG) from laboratory experiments to realistic scenarios or other experiments. Additionally, it often remains an open question whether actual cognitive workload reflected by brain signals was the main contribution to the estimation or discriminative and class-dependent muscle and eye activity, which can be secondary effects of changing workload levels. Within this study, we investigated a novel approach to spatial filtering based on beamforming adapted to changing settings. We compare it to no spatial filtering and Common Spatial Patterns (CSP). We used a realistic maneuvering task, as well as an auditory n-back secondary task on a tugboat simulator as two different conditions to induce workload changes on professional tugboat captains. Apart from the typical within condition classification, we investigated the ability of the different classification methods to transfer between the n-back condition and the maneuvering task. The results show a clear advantage of the proposed approach over the others in the challenging transfer setting. While no filtering leads to lowest within-condition normalized classification loss on average in two scenarios (22 and 10%), our approach using adaptive beamforming (30 and 18%) performs comparably to CSP (33 and 15%). Importantly, in the transfer from one to another setting, no filtering and CSP lead to performance around chance level (45 to 53%), while our approach in contrast is the only one capable of classifying in all other scenarios (34 and 35%) with a significant difference from chance level. The changing signal composition over the scenarios leads to a need to adapt the spatial filtering in order to be transferable. With our approach, the transfer is successful due to filtering being optimized for the extraction of neural components and additional investigation of their scalp patterns revealed mainly neural origin. Interesting findings are that rather the patterns slightly change between conditions. We conclude that the approaches with low normalized loss depend on eye and muscle activity which is successful for classification within conditions, but fail in the classifier transfer since eye and muscle contributions are highly condition-specific.

Job Assessment Through Bioelectrical Measures: A Neuromanagement Perspective

During recruitment, human resource departments face two challenges: finding the right people for the job and attracting talent. Therefore, the hiring process requires both the ability to communicate a good company brand image and to understand the characteristics and potential of candidates. In this study, we used a neuroscientific approach to measure the experience of candidates during a job interview. The experiment involved 30 participants that individually took part in a job interview lasting 40 min. During the experiment, their engagement and stress levels were measured in real-time with skin conductance and electroencephalographic (EEG) data. From the results, we identified both the most stressful phases (the second and the fourth parts, relating to the explanation of the job and remuneration) and the most engaging phases (the first and the third phases, relating to the presentation of the company and the explanation of the career process) of the interview, suggesting implications for the assessment process. This study is a contribution to the field of neuromanagement, as a neuroscientific approach was applied to management issues in light of work and organizational psychology.

Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.

Forefront Users' Experience Evaluation by Employing Together Virtual Reality and Electroencephalography: A Case Study on Cognitive Effects of Scents

Scents have the ability to affect peoples' mental states and task performance with to different extents. It has been widely demonstrated that the lemon scent, included in most all-purpose cleaners, elicits stimulation and activation, while the lavender scent elicits relaxation and sedative effects. The present study aimed at investigating and fostering a novel approach to evaluate users' experience with respect to scents' effects through the joint employment of Virtual Reality and users' neurophysiological monitoring, in particular Electroencephalography. In particular, this study, involving 42 participants, aimed to compare the effects of lemon and lavender scents on the deployment of cognitive resources during a daily life experience consisting in a train journey carried out in virtual reality. Our findings showed a significant higher request of cognitive resources during the processing of an informative message for subjects exposed to the lavender scent with respect to the lemon exposure. No differences were found between lemon and lavender conditions on the self-reported items of pleasantness and involvement; as this study demonstrated, the employment of the lavender scent preserves the quality of the customer experience to the same extent as the more widely used lemon scent.

An Exploration of Perceived Stress, Burnout Syndrome, and Self-Efficacy in a Group of Polish Air Traffic Controllers and Maritime Navigators: Similarities and Differences

Background: This cross-sectional study aimed to assess the level of perceived stress and occupational burnout in groups of Polish maritime navigators and air traffic controllers. The study was part of research into occupational groups regarded as equally emotionally burdened. We tested the usability of a model linking occupational burnout, perceived stress, and seniority. Methods: The set of questionnaires, including the link burnout questionnaire, perceived stress scale—10, and generalized self-efficacy scale were distributed to 54 maritime navigators and 88 air traffic controllers (rate of return: 18–56%). Spearman’s rho, χ² test, the Mann–Whitney U test, Cohen’s d and Hedge’s g coefficients, linear regression, and F statistic were used. Results: The assumption that persons employed in occupations with the special professional requirements as air traffic controllers and maritime navigator with a risk of strong, chronic emotional overload evaluate their life situation as less stressful than other employees was confirmed. A higher level of occupational burnout was observed in groups of controllers and navigators compared to an equally emotionally burdening occupational group of Polish firefighters, but not Polish psychiatrists. The research groups differed regarding the source of stress: fear of helplessness in the air traffic controller group and inefficacy in overcoming adversities in the maritime navigators. Maritime navigators reported a higher level of occupational burnout, deterioration of relations with coworkers, and disappointment with their work compared to the air traffic controllers. Conclusions: The results showed differences in factors linked to workplace demands and the personal predispositions of employees, and the role these may play in mutual relations between occupational burnout, life situation evaluation, and personal resources. We postulate that the level of perceived stress should be taken into account in the studies of occupational burnout syndrome.

Assessment of Athletes' Attitude: Physiological Evaluation via Wearable Sensors during Grappling Competitions

Sports activity is characterised by the influence of different factors, which relate to both psychological and emotional stress of athletes. As a consequence, mental and physical preparations are fundamental in pre-competition and competition activities. In fact, being able to manage the reactions to stressful events and high demanding conditions, and adapt the strategy depending on the ongoing situation and opponent's reactions allow the athletes to properly process the surrounding information, evaluate all the possible solutions, and finally take the right decision. In this regard, the Skin Conductance (SC), Heart Rate (HR), and Skin Temperature (ST) signals were recorded during a grappling tournament from ten athletes with the aim to investigate if physiological assessments could provide an objective measure of athletes' attitude. The results proved that individual training programs can be tailored accordingly to the neurophysiological state of the athletes, but also that their awareness about both mental and physical preparations and attitudes could be improved.