Multitasking While Driving: Central Bottleneck or Problem State Interference?

OBJECTIVE

The objective of this work was to investigate if visuospatial attention and working memory load interact at a central control resource or at a task-specific, information processing resource during driving.

BACKGROUND

In previous multitasking driving experiments, interactions between different cognitive concepts (e.g., attention and working memory) have been found. These interactions have been attributed to a central bottleneck or to the so-called problem-state bottleneck, related to working memory usage.

METHOD

We developed two different cognitive models in the cognitive architecture ACT-R, which implement the central vs. problem-state bottleneck. The models performed a driving task, during which we varied visuospatial attention and working memory load. We evaluated the model by conducting an experiment with human participants and compared the behavioral data to the model's behavior.

RESULTS

The problem-state-bottleneck model could account for decreased driving performance due to working memory load as well as increased visuospatial attentional demands as compared to the central-bottleneck model, which could not account for effects of increased working memory load.

CONCLUSION

The interaction between working memory and visuospatial attention in our dual tasking experiment can be best characterized by a bottleneck in the working memory. The model results suggest that as working memory load becomes higher, drivers manage to perform fewer control actions, which leads to decreasing driving performance.

APPLICATION

Predictions about the effect of different mental loads can be used to quantify the contribution of each subtask allowing for precise assessments of the current overall mental load, which automated driving systems may adapt to.

Return-to-Manual Performance can be Predicted Before Automation Fails

OBJECTIVE

This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control.

BACKGROUND

Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects.

METHOD

Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation.

RESULTS

For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance.

CONCLUSIONS

RTM performance degraded as trust in automation increased relative to participants' own average, but only for individuals with average or high levels of trust.

APPLICATIONS

Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements.

Work-Related Hazards and Perceived Confined-Space Health Risk : Understanding the Correlation with Mental Workload Among Farmers in Northern Thailand's Shallow Wells

OBJECTIVES

This study examined factors related to perceived health risks in confined spaces (PCSHR) and their correlation with the mental workload among farmers managing agricultural wells in northern Thailand.

METHODS

A cross-sectional, multi-stage sample of 356 farmers was selected from four rural districts' agricultural areas. Data were collected through interviews conducted from August to December 2022, using a self-administered structured questionnaire. The five-part questionnaire gathered demographic data, information on experiences and operations in agricultural wells, knowledge of confined spaces, PCSHR, and the six-dimension NASA Task Load Index (TLX) mental workload. Linear regression and multi-variable analyses were used to investigate factors associated with PCSHR, while Pearson correlations tested the association between PCSHR and mental workload variables.

RESULTS

Most farmers were male (92.4%), worked in wells to install pumping systems (81.7%) and maintain equipment (73.3%), averaging 3.80 times per year, with an average duration of 25.81 minutes. Physical symptoms reported included difficulty breathing (72.8%), feeling swelteringly hot (55.9%), and sweating excessively (27.8%), as well as accidents such as being struck by falling soil or objects (20.2%), and falling into the well while climbing down (14.9%). Farmers' perceived risk scores were high when working while physically exhausted or unprepared and when assisting an unconscious worker without knowing the gas concentration. In addition, the maximal mental workload scores were mental demand and effort subscale. Factors significantly associated with PCSHR (adj.R2 = 60.6%, p < .05) encompassed education higher than lower secondary level, current alcohol consumption, smaller well width, assisted operations, number of physical symptoms experienced, absence of environmental accidents, and confined space knowledge, while increased PCSHR was positively associated with mental workload (Overall r = 0.711, p < .01).

CONCLUSION

Comprehensive education about potential hazards can improve farmers' risk perception, potentially reducing mental workload and preventing fatal accidents. Field studies are recommended to develop community-specific work protocols and accurate measuring instruments suitable for rural settings are needed.

Swimming with a head-mounted display: dual-task costs

Head-up displays (HUDs) have the potential to change work in operation environments by providing hands-free information to wearers. However, these benefits may be accompanied by trade-offs, primarily by increasing cognitive load due to dividing attention. Previous studies have attempted to understand the trade-offs of HUD usage; however, all of which were focused on land-based tasks. A gap in understanding exists when examining HUD use in aquatic environments as immersion introduces unique environmental and physiological factors that could affect multitasking. In this study, we investigated multitasking performance associated with swimming with a HUD. Eighteen participants completed three tasks: swimming only, a HUD-administered word recall task, and a dual-task combining both tasks. Results revealed significant dual-task interference in both tasks, though possibly less pronounced than in land-based tasks. These findings enhance not only help characterise dual-task performance, but also offer valuable insights for HUD design for aquatic settings.

Reproducible machine learning research in mental workload classification using EEG

This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.

Vigilance to Spatialized Auditory Displays: Initial Assessment of Performance and Workload

OBJECTIVE

The present study was designed to evaluate human performance and workload associated with an auditory vigilance task that required spatial discrimination of auditory stimuli.

BACKGROUND

Spatial auditory displays have been increasingly developed and implemented into settings that require vigilance toward auditory spatial discrimination and localization (e.g., collision avoidance warnings). Research has yet to determine whether a vigilance decrement could impede performance in such applications.

METHOD

Participants completed a 40-minute auditory vigilance task in either a spatial discrimination condition or a temporal discrimination condition. In the spatial discrimination condition, participants differentiated sounds based on differences in spatial location. In the temporal discrimination condition, participants differentiated sounds based on differences in stimulus duration.

RESULTS

Correct detections and false alarms declined during the vigilance task, and each did so at a similar rate in both conditions. The overall level of correct detections did not differ significantly between conditions, but false alarms occurred more frequently within the spatial discrimination condition than in the temporal discrimination condition. NASA-TLX ratings and pupil diameter measurements indicated no differences in workload.

CONCLUSION

Results indicated that tasks requiring auditory spatial discrimination can induce a vigilance decrement; and they may result in inferior vigilance performance, compared to tasks requiring discrimination of auditory duration.

APPLICATION

Vigilance decrements may impede performance and safety in settings that depend on sustained attention to spatial auditory displays. Display designers should also be aware that auditory displays that require users to discriminate differences in spatial location may result in poorer discrimination performance than non-spatial displays.

An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool

OBJECTIVE

This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS).

BACKGROUND

The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL.

METHOD

The MWL-AA presented in this study was a semi-autonomous suction tool. The first experiment recruited ten participants to perform surgical tasks under different MWL levels. The physiological responses were captured and used to develop a real-time multi-sensing model for MWL detection. The second experiment evaluated the effectiveness of the MWL-AA, where nine brand-new surgical trainees performed the surgical task with and without the MWL-AA. Mixed effect models were used to compare task performance, objective- and subjective-measured MWL.

RESULTS

The proposed system predicted high MWL hemorrhage conditions with an accuracy of 77.9%. For the MWL-AA evaluation, the surgeons' gaze behaviors and brain activities suggested lower perceived MWL with MWL-AA than without. This was further supported by lower self-reported MWL and better task performance in the task condition with MWL-AA.

CONCLUSION

A MWL-AA systems can reduce surgeons' workload and improve performance in a high-stress hemorrhaging scenario. Findings highlight the potential of utilizing MWL-AA to enhance the collaboration between the autonomous system and surgeons. Developing a robust and personalized MWL-AA is the first step that can be used do develop additional use cases in future studies.

APPLICATION

The proposed framework can be expanded and applied to more complex environments to improve human-robot collaboration.

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration

Objective. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC.Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials.Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error.Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

Investigating the relationship between physical, cognitive, and environmental factors of ergonomics with the prevalence of musculoskeletal disorders: A case study in a car-parts manufacturing industry

BACKGROUND

Work-related musculoskeletal disorders (WRMSDs) is a multi-factorial disorder in most occupational setting and it has increased significantly in recent years.

OBJECTIVE

This study aimed to investigate the relationship between physical, cognitive, and environmental factors of ergonomics with the prevalence of WRMSDs in a car-parts manufacturing industry.

METHODS

This cross-sectional study was performed among 220 workers in a milling unit of a car parts manufacturing company in 2021-2022. The prevalence of WRMSDs was assessed using the Extended Version of the Nordic Musculoskeletal Questionnaire. Noise exposure was evaluated using dosimetry method. Mental and physical workload were evaluated by the NASA-TLX and key index methods (KIM-MHO and KIM-LHC), respectively. Data analysis was performed using SPSS version 25.0.

RESULTS

The subjects' mean age and work experience were 36.3±6.5 and 8.35±6.41 years, respectively. Eighty-five percent of the subjects reported WRMSDs in at least one area of the body. The results of mental workload assessment revealed a high workload mean range (73.23±14.89) in all of the subjects. Mean score of KIM-LHC and KIM-MHO were 738.18±336.42 and 201.86±36.41, respectively with odds ratio of 1.32 for KIM-LHC in creating the WRMSDs. There was a significant relationship between the noise exposure, mental and physical workload and the prevalence of WRMSDs (p-value <  0.05).

CONCLUSION

The results of the present study revealed that environmental, physical and cognitive factors can simultaneously be effective in the prevalence of WRMSDs. Therefore, performing effective control measures requires comprehensive attention to physical, environmental, and cognitive ergonomics in the algorithm of ergonomics management in the workplace.

Recognition and evaluation of mental workload in different stages of perceptual and cognitive information processing using a multimodal approach

This study explores the effects of different perceptual and cognitive information processing stages on mental workload by assessing multimodal indicators of mental workload such as the NASA-TLX, task performance, ERPs and eye movements. Repeated measures ANOVA of the data showed that among ERP indicators, P1, N1 and N2 amplitudes were sensitive to perceptual load (P-load), P3 amplitude was sensitive to P-load only in the prefrontal region during high cognitive load (C-load) states, and P3 amplitude in the occipital and parietal regions was sensitive to C-load. Among the eye movement indicators, blink frequency was sensitive to P-load in all C-load states, but to C-load in only low P-load states; pupil diameter and blink duration were sensitive to both P-load and C-load. Based on the above indicators, the k-nearest neighbours (KNN) algorithm was used to propose a classification method for the four different mental workload states with an accuracy of 97.89%.Practitioner summary: Based on the results of this study, it is possible to implement the monitoring of mental workload states and optimise brain task allocation in operations involving high mental workload, such as human-computer interaction.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking

OBJECTIVE

(1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU.

BACKGROUND

Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data.

METHODS

Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes.

RESULTS

While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter.

CONCLUSION

Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development.

Granular estimation of user cognitive workload using multi-modal physiological sensors

Mental workload (MWL) is a crucial area of study due to its significant influence on task performance and potential for significant operator error. However, measuring MWL presents challenges, as it is a multi-dimensional construct. Previous research on MWL models has focused on differentiating between two to three levels. Nonetheless, tasks can vary widely in their complexity, and little is known about how subtle variations in task difficulty influence workload indicators. To address this, we conducted an experiment inducing MWL in up to 5 levels, hypothesizing that our multi-modal metrics would be able to distinguish between each MWL stage. We measured the induced workload using task performance, subjective assessment, and physiological metrics. Our simulated task was designed to induce diverse MWL degrees, including five different math and three different verbal tiers. Our findings indicate that all investigated metrics successfully differentiated between various MWL levels induced by different tiers of math problems. Notably, performance metrics emerged as the most effective assessment, being the only metric capable of distinguishing all the levels. Some limitations were observed in the granularity of subjective and physiological metrics. Specifically, the subjective overall mental workload couldn't distinguish lower levels of workload, while all physiological metrics could detect a shift from lower to higher levels, but did not distinguish between workload tiers at the higher or lower ends of the scale (e.g., between the easy and the easy-medium tiers). Despite these limitations, each pair of levels was effectively differentiated by one or more metrics. This suggests a promising avenue for future research, exploring the integration or combination of multiple metrics. The findings suggest that subtle differences in workload levels may be distinguishable using combinations of subjective and physiological metrics.

EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.

Modality Changes in Vigilance Displays: Further Evidence of Supramodal Resource Depletion in Vigilance

OBJECTIVE

This study was designed to evaluate the effects of a modality change on vigilance performance to determine whether depletion of modality-specific resources contributes to the vigilance decrement.

BACKGROUND

Resource theory accounts for the vigilance decrement by arguing that the demands of vigilance deplete limited information processing resources. Research indicates that both supramodal and modality-specific resources are involved in vigilance, but it is unclear whether the vigilance decrement is due to depletion of supramodal resources, modality-specific resources, or both. If depletion of modality-specific resources contributes to the decrement, changing the modality of a vigilance display should improve vigilance performance after a decrement.

METHOD

Participants completed a 50-min vigilance task beginning in either the visual modality or the auditory modality. After 40-min, half of the participants experienced a sudden transition to the other modality; the remaining participants did not experience a modality change.

RESULTS

Performance declined over time and was generally superior in the auditory modality. Changing modality from visual to auditory increased correct detections, whereas changing from auditory to visual decreased correct detections. Both types of modality change were associated with an increase in false alarms, and neither had an effect on workload or stress.

CONCLUSION

Supramodal resource depletion, rather than modality-specific resource depletion, is the most likely explanation for the vigilance decrement that can be derived from resource theory.

APPLICATION

Modality changes are not likely to counteract the vigilance decrement and may actually increase false alarm errors. Countermeasure development should involve identification of depleted supramodal resources.

A Review of Cognitive Support Systems in the Operating Room

BACKGROUND

In recent years, numerous innovative yet challenging surgeries, such as minimally invasive procedures, have introduced an overwhelming amount of new technologies, increasing the cognitive load for surgeons and potentially diluting their attention. Cognitive support technologies (CSTs) have been in development to reduce surgeons' cognitive load and minimize errors. Despite its huge demands, it still lacks a systematic review.

METHODS

Literature was searched up until May 21st, 2021. Pubmed, Web of Science, and IEEExplore. Studies that aimed at reducing the cognitive load of surgeons were included. Additionally, studies that contained an experimental trial with real patients and real surgeons were prioritized, although phantom and animal studies were also included. Major outcomes that were assessed included surgical error, anatomical localization accuracy, total procedural time, and patient outcome.

RESULTS

A total of 37 studies were included. Overall, the implementation of CSTs had better surgical performance than the traditional methods. Most studies reported decreased error rate and increased efficiency. In terms of accuracy, most CSTs had over 90% accuracy in identifying anatomical markers with an error margin below 5 mm. Most studies reported a decrease in surgical time, although some were statistically insignificant.

DISCUSSION

CSTs have been shown to reduce the mental workload of surgeons. However, the limited ergonomic design of current CSTs has hindered their widespread use in the clinical setting. Overall, more clinical data on actual patients is needed to provide concrete evidence before the ubiquitous implementation of CSTs.

Orientation Comes First: Becoming Aware of Spatial Disorientation Interferes with Cognitive Performance

BACKGROUND

Previous research has shown that experiencing motion stimuli negatively impacts cognitive performance.

OBJECTIVE

In the current study, we investigate whether this impact relates to Type-II spatial disorientation (SD), to motion stimulus magnitude, or to an interaction of these factors.

METHOD

Stimuli for participants (n = 23) consisted of Earth-vertical yaw rotations on a rotating chair in a completely darkened room. In the surprise condition, the stimulus started with subthreshold acceleration, followed by suprathreshold deceleration to a non-zero velocity, inducing a sensation of rotation that is opposite to the actual rotation revealed when the lights were switched on. In the no-surprise condition, the same changes in velocity were used, but starting from (almost) zero velocity, which induced a sensation of rotation in the same direction as the actual rotation. Participants performed a self-paced arithmetic task and measurement of their cognitive performance started after the environment was revealed. Stimulus magnitude was operationalized through higher or lower peak suprathreshold deceleration.

RESULTS

The results revealed that counting speed decreased significantly when participants were surprised, constituting a large effect size. The proportion of counting errors likewise increased significantly when participants were surprised, but only in the high-magnitude condition.

APPLICATION

The findings suggest that surprise caused by the recognition of SD has an involuntary disruptive effect on cognition, which may impact performance of piloting tasks. These results are relevant when modeling motion stimuli effects on performance, and when developing SD awareness training for pilots.

Cognitive mental workload of emergency nursing: A scoping review

AIM

Emergency nurses work in an environment of high cognitive mental workload. Excessive cognitive mental workload may result in patient harm and nurses' burnout. Therefore, it is necessary to understand nurses' subjective experience of cognitive workload. This scoping review aimed to curate literature about the subjective experience of cognitive mental workload reported by nurses and psychometric measures of the phenomenon.

DESIGN

The scoping review was conducted in accordance with JBI methodology and reported using PRISMA extension for scoping review checklist.

METHODS

A priori protocol was created with Peer Review of Electronic Search Strategies checklist and registered in the OSF registry. Databases including PubMed, CINAHL, ProQuest, Scopus, Science Direct, Web of Science and Google Scholar were searched. Published reports were reviewed against the eligibility criteria by performing Title and Abstract screening, followed by Full-text screening. The initial search yielded 1373 studies. Of these, 57 studies met the criteria for inclusion in this study.

RESULTS

The search revealed five general measures of cognitive mental workload and their variations. Only one customised measure specifically for medical-surgical nurses was found in the study. Identified measures were collated and categorised into a framework for conceptual clarity. NASA Task Load Index and its variations were the most popular subjective measure of cognitive mental workload in nursing. However, no measure or self-report scale customised for emergency nurses was identified.

PATIENT OR PUBLIC CONTRIBUTION

The findings of this scoping review can inform future research into the cognitive mental workload of nurses. The findings have implications for workplace health and safety for nurses and patients.

Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG Study

While the term task load (TL) refers to external task demands, the amount of work, or the number of tasks to be performed, mental workload (MWL) refers to the individual's effort, mental capacity, or cognitive resources utilized while performing a task. MWL in multitasking scenarios is often closely linked with the quantity of tasks a person is handling within a given timeframe. In this study, we challenge this hypothesis from the perspective of electroencephalography (EEG) using a deep learning approach. We conducted an EEG experiment with 50 participants performing NASA Multi-Attribute Task Battery II (MATB-II) under 4 different task load levels. We designed a convolutional neural network (CNN) to help with two distinct classification tasks. In one setting, the CNN was used to classify EEG segments based on their task load level. In another setting, the same CNN architecture was trained again to detect the presence of individual MATB-II subtasks. Results show that, while the model successfully learns to detect whether a particular subtask is active in a given segment (i.e., to differentiate between different subtasks-related EEG patterns), it struggles to differentiate between the two highest levels of task load (i.e., to distinguish MWL-related EEG patterns). We speculate that the challenge comes from two factors: first, the experiment was designed in a way that these two highest levels differed only in the quantity of work within a given timeframe; and second, the participants' effective adaptation to increased task demands, as evidenced by low error rates. Consequently, this indicates that under such conditions in multitasking, EEG may not reflect distinct enough patterns to differentiate higher levels of task load.

Driver behavior and mental workload for takeover safety in automated driving: ACT-R prediction modeling approach

OBJECTIVE

Conditional automated driving (SAE level 3) requires the driver to take over the vehicle if the automated system fails. The mental workload that can occur in these takeover situations is an important human factor that can directly affect driver behavior and safety, so it is important to predict it. Therefore, this study introduces a method to predict mental workload during takeover situations in automated driving, using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. The mental workload prediction model proposed in this study is a computational model that can become the basis for emerging crash avoidance technologies in future autonomous driving situations.

METHODS

The methodology incorporates the ACT-R cognitive architecture, known for its robustness in modeling cognitive processes and predicting performance. The proposed takeover cognitive model includes the symbolic structure for repeatedly checking the driving situation and performing decision-making for takeover as well as Non-Driving-Related Tasks (NDRT). We employed the ACT-R cognitive model to predict mental workload during takeover in automated driving scenarios. The model's predictions are validated against physiological data and performance data from the validation test.

RESULTS

The model demonstrated high accuracy, with an r-square value of 0.97, indicating a strong correlation between the predicted and actual mental workload. It successfully captured the nuances of multitasking in driving scenarios, showcasing the model's adaptability in representing diverse cognitive demands during takeover.

CONCLUSIONS

The study confirms the efficacy of the ACT-R model in predicting mental workload for takeover scenarios in automated driving. It underscores the model's potential in improving driver-assistance systems, enhancing vehicle safety, and ensuring the efficient integration of human-machine roles. The research contributes significantly to the field of cognitive modeling, providing robust predictions and insights into human behavior in automated driving tasks.

How flight experience impacts pilots' decision-making and visual scanning pattern in low-visibility approaches: preliminary evidence from eye tracking

The visual approach is the most accident-prone phase of a flight, especially in low-visibility conditions. This preliminary study aimed to examine the effects of flight experience on pilots' decision-making and visual scanning pattern in low-visibility approaches. Twenty pilots were separated into two groups based on their flight experience and completed the high- and low-visibility approaches in balanced order using a high-fidelity flight simulator. Pilots' mental workload and visual scanning patterns were recorded via an eye tracker. The results showed that, compared to less flight-experienced pilots (20%, 3/15), experienced pilots (80%, 4/5) were more likely to make go-around decisions in the low-visibility approaches. Furthermore, they exhibited a more flexible and adaptable visual scanning pattern by quickly shifting their attention, as evidenced by decreased fixations and increased saccades. These findings suggest that the integration of visual scanning strategy and training solution with a marginally meteorological approach may enhance decision-making safety for novice pilots.

Investigating mental workload caused by NDRTs in highly automated driving with deep learning

OBJECTIVE

This study aimed to examine the impact of non-driving-related tasks (NDRTs) on drivers in highly automated driving scenarios and sought to develop a deep learning model for classifying mental workload using electroencephalography (EEG) signals.

METHODS

The experiment involved recruiting 28 participants who engaged in simulations within a driving simulator while exposed to 4 distinct NDRTs: (1) reading, (2) listening to radio news, (3) watching videos, and (4) texting. EEG data collected during NDRTs were categorized into 3 levels of mental workload, high, medium, and low, based on the NASA Task Load Index (NASA-TLX) scores. Two deep learning methods, namely, long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM), were employed to develop the classification model.

RESULTS

A series of correlation analyses revealed that the channels and frequency bands are linearly correlated with mental workload. The comparative analysis of classification results demonstrates that EEG data featuring significantly correlated frequency bands exhibit superior classification accuracy compared to the raw EEG data.

CONCLUSIONS

This research offers a reference for assessing mental workload resulting from NDRTs in the context of highly automated driving. Additionally, it delves into the development of deep learning classifiers for EEG signals with heightened accuracy.

Towards ubiquitous and nonintrusive measurements of brain function in the real world: assessing blink-related oscillations during simulated flight using portable low-cost EEG

Blink-related oscillations (BRO) are newly discovered neurophysiological phenomena associated with spontaneous blinking and represent cascading neural mechanisms including visual sensory, episodic memory, and information processing responses. These phenomena have been shown to be present at rest and during tasks and are modulated by cognitive load, creating the possibility for brain function assessments that can be integrated seamlessly into real-world settings. Prior works have largely examined the BRO phenomenon within controlled laboratory environments using magnetoencephalography and high-density electroencephalography (EEG) that are ill-suited for real-world deployment. Investigating BROs using low-density EEG within complex environments reflective of the real-world would further our understanding of how BRO responses can be utilized in real-world settings. We evaluated whether the BRO response could be captured in a high-fidelity flight simulation environment using a portable, low-density wireless EEG system. The effects of age and task demands on BRO responses were also examined. EEG data from 30 licensed pilots (age 43.37 +/- 17.86, 2 females) were collected during simulated flights at two cognitive workload levels. Comparisons of signal amplitudes were undertaken to confirm the presence of BRO responses and mixed model ANOVAs quantified the effects of workload and age group on BRO amplitudes. Significant increases in neural activity were observed post-blink compared to the baseline period (p < 0.05), confirming the presence of BRO responses. In line with prior studies, results showed BRO time-domain responses from the delta band (0.5-4 Hz) consisting of an early negative peak followed by a positive peak post-blink in temporal and parietal electrodes. Additionally, task workload and age-related effects were also found, with observations of the enhancement of BRO amplitudes with older age and attenuation of BRO responses in high workloads (p < 0.05). These findings demonstrate that it is possible to capture BRO responses within simulated flight environments using portable, low-cost, easy-to-use EEG systems. Furthermore, biological and task salience were reflected in these BRO responses. The successful detection and demonstration of both task-and age-related modulation of BRO responses in this study open the possibility of assessing human brain function across the lifespan with BRO responses in complex and realistic environments.

Evidence for modulation of EEG microstates by mental workload levels and task types

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

Human Robot Collaboration for Enhancing Work Activities

OBJECTIVE

Trade-offs between productivity, physical workload (PWL), and mental workload (MWL) were studied when integrating collaborative robots (cobots) into existing manual work by optimizing the allocation of tasks.

BACKGROUND

As cobots become more widely introduced in the workplace and their capabilities greatly improved, there is a need to consider how they can best help their human partners.

METHODS

A theoretical data-driven analysis was conducted using the O*NET Content Model to evaluate 16 selected jobs for associated work context, skills, and constraints. Associated work activities were ranked by potential for substitution by a cobot. PWL and MWL were estimated using variables from the O*Net database that represent variables for the Strain Index and NASA-TLX. An algorithm was developed to optimize work activity assignment to cobots and human workers according to their most suited abilities.

RESULTS

Human workload for some jobs decreased while workload for some jobs increased after cobots were reassigned tasks, and residual human capacity was used to perform job activities designated the most important to increase productivity. The human workload for other jobs remained unchanged.

CONCLUSIONS

The changes in human workload from the introduction of cobots may not always be beneficial for the human worker unless trade-offs are considered.Application: The framework of this study may be applied to existing jobs to identify the relationship between productivity and worker tolerances that integrate cobots into specific tasks.

Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum

Objective. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings.Approach. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance.Main results. Compared to the raw PSD (69.9% ± 18.5%) and the aperiodic component (69.4% ± 19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2% ± 11.0%).Significance. These findings indicate that periodic components of EEG have the potential to be applied in decoding brain states for more robust pBCIs.

Advanced workstations and collaborative robots: exploiting eye-tracking and cardiac activity indices to unveil senior workers' mental workload in assembly tasks

Introduction: As a result of Industry 5.0's technological advancements, collaborative robots (cobots) have emerged as pivotal enablers for refining manufacturing processes while re-focusing on humans. However, the successful integration of these cutting-edge tools hinges on a better understanding of human factors when interacting with such new technologies, eventually fostering workers' trust and acceptance and promoting low-fatigue work. This study thus delves into the intricate dynamics of human-cobot interactions by adopting a human-centric view. Methods: With this intent, we targeted senior workers, who often contend with diminishing work capabilities, and we explored the nexus between various human factors and task outcomes during a joint assembly operation with a cobot on an ergonomic workstation. Exploiting a dual-task manipulation to increase the task demand, we measured performance, subjective perceptions, eye-tracking indices and cardiac activity during the task. Firstly, we provided an overview of the senior workers' perceptions regarding their shared work with the cobot, by measuring technology acceptance, perceived wellbeing, work experience, and the estimated social impact of this technology in the industrial sector. Secondly, we asked whether the considered human factors varied significantly under dual-tasking, thus responding to a higher mental load while working alongside the cobot. Finally, we explored the predictive power of the collected measurements over the number of errors committed at the work task and the participants' perceived workload. Results: The present findings demonstrated how senior workers exhibited strong acceptance and positive experiences with our advanced workstation and the cobot, even under higher mental strain. Besides, their task performance suffered increased errors and duration during dual-tasking, while the eye behavior partially reflected the increased mental demand. Some interesting outcomes were also gained about the predictive power of some of the collected indices over the number of errors committed at the assembly task, even though the same did not apply to predicting perceived workload levels. Discussion: Overall, the paper discusses possible applications of these results in the 5.0 manufacturing sector, emphasizing the importance of adopting a holistic human-centered approach to understand the human-cobot complex better.

Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes

Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.

Speed behaviour and mental workload of small-spacing expressway interchanges based on field driving test

Many small-spacing interchanges (SSI) appear with the improvement of the expressway network. To investigate the speed and mental workload characteristics in the SSI and acquire the mechanism of the influence of speed on the drivers' workload, 37 participants were recruited to perform a field driving test. Each driver performed four driving conditions (i.e. ramp-mainline, mainline-ramp, mainline driving, and auxiliary lane driving). The speed and drivers' electrocardiogram (ECG) data were collected using SpeedBox speed acquisition equipment and PhysioLAB physiological instrument. The heart rate increase (HRI) index was used to analyse the drivers' mental workload regularity. The relationship model between speed and HRI was developed to examine the impact of speed on HRI. The results show that the speed variation in the SSI displayed two patterns: 'decrease - increase and continuous decrease.' The drivers' HRI variation presented four patterns: 'convex curve, continuously increasing, continuously decreasing and concave curve'. SSI's influenced area length is given based on the speed and HRI variation regularity. HRI is significantly higher when driving in the ramp-mainline condition in the SSI than when driving in other conditions, indicating that drivers are more nervous when merging with the mainline traffic. HRI increases significantly in the first 50% of the weaving area in four driving conditions, indicating that vehicle weaving greatly influences the drivers' mental workload. A positive correlation exists between vehicle speed and drivers' HRI without interference from other vehicles and road alignment.

Simulated skill complexity and perceived cognitive load during preclinical dental training

INTRODUCTION

Cognitive Load theory (CLT) focuses on the information processing aspect of learning and how the working memory handles the mental effort associated with new task. The aim of this study is to investigate the association between the perceived cognitive load and performance amongst dental students during preclinical simulation training at various levels of procedural task complexity. Additionally, some cognitive load-modifying factors were examined.

MATERIALS AND METHODS

This cross-sectional study evaluated the perceived cognitive load amongst second-year dental students (n = 34), using the validated National Aeronautics and Space Administration's Task Load Index (NASA TLX index) after training on four dental tasks at two levels of complexity, in addition to structured online anonymous questionnaire about demographics, feedback and performance. The NASA TLX raw scores and the weighted global score were calculated for each exercise. Descriptive statistics and Pearson's correlations between performance and the corresponding NASA TLX-weighted score were calculated. Mean differences in the perceived cognitive load across the exercise levels were assessed using RM-ANOVA with Bonferroni corrections at p < .05.

RESULTS

Reduced performance was significantly associated with higher cognitive load particularly in high complexity dental task (class II-mirror vision). Simulated exercise complexity significantly influenced the students' perceived mental demand, physical demand and temporal demand; all were significantly higher for class II- mirror vision task than for direct vision tasks. The majority of participants (82.1%) preferred detailed feedback from instructors, and more than half of the participants (60.7%) preferred continuous feedback throughout the training session.

CONCLUSION

Complex dental tasks are associated with higher cognitive load in novice dental students during preclinical training. The NASA TLX index is a useful instrument to explore the level of perceived cognitive load associated with performance of simulated complex dental skills. Cognitive load theory is relevant to simulation-based dental education to improve the preclinical instructional efficiency and to enhance students learning.

The levels and related factors of mental workload among nurses: A systematic review and meta-analysis

AIM

The aim was to determine the overall levels and related factors of mental workload assessed using the NASA-TLX tool among nurses.

BACKGROUND

Mental workload is a key element that affects nursing performance. However, there exists no review regarding mental workload assessed using the NASA-TLX tool, focusing on nurses.

DESIGN

A systematic review and meta-analysis.

DATA SOURCES

PubMed, MEDLINE, Web of Science, EMBASE, PsycINFO, Scopus, CINAHL, CNKI, CBM, Weipu and WanFang databases were searched from 1 January 1998 to 30 February 2022.

REVIEW METHODS

Following the PRISMA statement recommendations, review methods resulted in 31 quantitative studies retained for inclusion which were evaluated with the evaluation criteria for observational studies as recommended by the Agency for Healthcare Research and Quality. The data were pooled and a random-effects meta-analysis conducted.

RESULTS

Findings showed the pooled mental workload score was 65.24, and the pooled prevalence of high mental workload was 54%. Subgroup analysis indicated nurses in developing countries and emergency departments experienced higher mental workloads, and the mental workloads of front-line nurses increased significantly during the COVID-19 pandemic.

CONCLUSION

These findings highlight that nurses experience high mental workloads as assessed using the NASA-TLX tool and there is an urgent need to explore interventions to decrease their mental workloads.

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving

OBJECTIVE

This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement.

BACKGROUND

Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time.

METHOD

Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions-with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded.

RESULTS

DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload-though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation.

CONCLUSION

Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level.

APPLICATION

These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.

Neurophysiological Assessment of An Innovative Maritime Safety System in Terms of Ship Operators' Mental Workload, Stress, and Attention in the Full Mission Bridge Simulator

The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks.

Research on Mental Workload of Deep-Sea Oceanauts Driving Operation Tasks from EEG Data

A person's present mental state is closely associated with the frequency and temporal domain features of spontaneous electroencephalogram (EEG) impulses, which directly reflect neurophysiological signals of brain activity. EEG signals are employed in this study to measure the mental workload of drivers while they are operating a vehicle. A technique based on the quantum genetic algorithm (QGA) is suggested for improving the kernel function parameters of the multi-class support vector machine (MSVM). The performance of the algorithm based on the quantum genetic algorithm is found to be superior to that of other ways when other methods and the quantum genetic algorithm are evaluated for the parameter optimization of kernel function via simulation. A multi-classification support vector machine based on the quantum genetic algorithm (QGA-MSVM) is applied to identify the mental workload of oceanauts through the collection and feature extraction of EEG signals during driving simulation operation experiments in a sea basin area, a seamount area, and a hydrothermal area. Even with a limited data set, QGA-MSVM is able to accurately identify the cognitive burden experienced by ocean sailors, with an overall accuracy of 91.8%.

The effect of mobile phone ringtone on visual recognition during driving: Evidence from laboratory and real-scene eye movement experiments

OBJECTIVE

To determine the effect of mobile phone ringtones on visual recognition during driving, laboratory and real-scene eye movement experiments were conducted with simulated and real driving tasks, respectively. Competition for visual attention during driving increases with the integration of sounds, which is related to driving safety.

METHOD

We manipulated the physical (long exposure duration vs. short exposure duration) and psychological (self-related vs. non-self-related) properties of mobile phone ringtones presented to drivers. Estimates were based on linear mixed models (LMMs) and generalized linear mixed models (GLMMs).

RESULTS

Self-related ringtones had a greater influence on driving attention than non-self-related ones, and the interaction between exposure duration and self-relatedness was significant. Furthermore, the impact of the mobile phone ringtone occurred in real time after the ringtone stopped.

CONCLUSION

These results highlight the importance of considering the impact of ringtones on driving performance and demonstrate that ringtone properties (exposure duration and self-relatedness) can affect cognitive processes.

Exploring the influence of individual factors on the perception of mental workload and body postures

Studies have revealed that physical and mental demands, psychosocial factors, and individual factors can contribute to the development of WMSDs. Yet, much is still unknown regarding the effects of individual characteristics on WMSDs susceptibility. Previous studies discovered people assumed more awkward body postures to perform an activity when the perception of mental workload is higher. This research study explored if individual characteristics such as age, sex, personality, and anxiety help explain changes or differences in the perception of mental workload and body postures assume when performing activities. The study provided evidence that these individual characteristics have a modifying role on perceived mental workload and body postures. The results suggest that perceived mental workload is influenced to a higher extent by individual characteristics such as anxiety, sex, and personality traits. Women have a higher (18.7%) mental workload perception than men. Likewise, NASA-TLX scores are 22% higher for feelers than thinkers. In general, higher perceptions of mental workload were observed in participants with higher anxiety levels. On the other hand, body postures seem to be influenced by different individual factors depending on the nature of the activity. RULA scores increased on average by 13.1% between baseline and time constraint conditions. Larger differences were observed in certain individuals (e.g. introverts (19.7%) and intuitors (13.8%)) across conditions.

Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient

Artificial intelligence and telemedicine promise to reshape patient care to an unprecedented extent, leading to a safer and more sustainable work environment and improved patient care. In this article, we summarize how these emerging technologies can be used in the care of cardiovascular patients in such ways as fall detection and prevention, virtual nursing, remote case support, automation of instrument counts in the operating room, and efficiency optimization in the cardiovascular suite.

Measured task load in directed observers versus active participants undergoing high-fidelity simulation education in a critical care setting

Objectives

The use of directed observers in high-fidelity simulation education is increasingly common. While evidence suggests similar educational outcomes for directed observers compared to active participants in technical skills, it remains uncertain if this benefit also exists for senior clinicians, especially in mental workload. We sought to compare the workload between active participants and directed observers using an objective measure.

Methods

We performed a prospective, repeated-measures observational study during the New South Wales Ambulance Aeromedical Operations induction training from 2019 to 2020. Participants included senior critical care doctors, paramedics, and nurses undergoing high-fidelity simulation of prehospital and interhospital aeromedical missions. Task load was measured using the National Aeronautics and Space Administration task load index (NASA-TLX) administered following each simulation debrief. Prehospital and interhospital simulations were compared separately by building a multilevel model for complete case and all study data. Post hoc comparisons of NASA-TLX score for each group were performed using estimated marginal means (EMMs).

Results

We enrolled 70 participants, comprising 49 physicians (70%), 19 paramedics (27%), and two flight nurses (3%). From the complete case analysis, statistically significant differences were observed for total NASA-TLX scores between active participants and directed observers in both prehospital (participant EMM 78, observer EMM 65, estimated difference -13, 95% confidence interval [CI] -20 to -7) and interhospital simulations (participant EMM 69, observer EMM 59, estimated difference -10, 95% CI -16 to -3). When all available data were included, the pattern of results did not change.

Conclusions

In our sample of senior clinicians, the task load experienced by both active participants and directed observers in high-fidelity simulation education was high for both prehospital and interhospital simulation exercises. The statistically significant differences we report are unlikely to be practically significant. Our results support the use of directed observers when resource limitations do not allow all course attendees to participate in every simulation.

Detection of Pilot's Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots' real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system's performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

Degree of Alarm Fatigue and Mental Workload of Hospital Nurses in Intensive Care Units

This study aimed to determine the degree of alarm fatigue and mental workload of ICU nurses, and to clarify the relationship between these two variables. A cross-sectional, descriptive research design was used. Data were collected from 90 nurses working in four ICUs in Seoul, Republic of Korea, using a questionnaire determining their degree of alarm fatigue and mental workload. Data were collected from 6 March to 26 April 2021 and were analyzed using a t-test, ANOVA, and Pearson's correlation coefficient. The average alarm-fatigue score was 28.59 out of 44. The item with the highest score was "I often hear a certain amount of noise in the ward", with a score of 3.59 out of 4. The average of the mental workload scores was 75.21 out of 100. The highest mental workload item was effort, which scored 78.72 out of 100. No significant correlation was found between alarm fatigue and mental workload. Although nurses were consistently exposed to alarm fatigue, this was not directly related to their mental workloads, perhaps owing to their professional consciousness as they strived to accomplish tasks despite alarm fatigue. However, since alarm fatigue can affect efficiency, investigations to reduce it and develop appropriate guidelines are necessary. This study was not registered.

Levels and influencing factors of mental workload among intensive care unit nurses: A systematic review and meta-analysis

AIM

The purpose of this systematic review was to determine the levels and influencing factors of mental workload in intensive care unit nurses.

BACKGROUND

Intensive care unit nurses have a high mental workload level. To our knowledge, no meta-analytic research investigating the levels of mental workload in intensive care unit nurses and related factors has yet been performed.

DESIGN

This article is a systematic review and meta-analysis.

METHODS

Eleven electronic databases were searched from the database setup dates until 31 December 2022. The research team independently conducted study selection, quality assessments, data extractions and analysis of all included studies. The PRISMA guideline was used to guide reportage of the systematic review and meta-analysis.

RESULTS

Seventeen studies were included. In these studies, the pooled mean score of mental workload was 68.07 (95%CI:64.39-71.75). Furthermore, subgroup analyses indicated that intensive care unit nurses' mental workload differed significantly by countries, sample size and publication year. The mental workload influential factors considered were demographic, work-related and psychological factors.

CONCLUSION

Hospital administrators should develop interventions to reduce mental workload to enhance the mental health of intensive care unit nurses and nursing care quality. Hospital managers should pay attention to the mental health of nurses and guide them to correctly relieve occupational stress and reduce mental workload.

An Examination of the Effects of Virtual Reality Training on Spatial Visualization and Transfer of Learning

Spatial visualization ability (SVA) has been identified as a potential key factor for academic achievement and student retention in Science, Technology, Engineering, and Mathematics (STEM) in higher education, especially for engineering and related disciplines. Prior studies have shown that training using virtual reality (VR) has the potential to enhance learning through the use of more realistic and/or immersive experiences. The aim of this study was to investigate the effect of VR-based training using spatial visualization tasks on participant performance and mental workload using behavioral (i.e., time spent) and functional near infrared spectroscopy (fNIRS) brain-imaging-technology-derived measures. Data were collected from 10 first-year biomedical engineering students, who engaged with a custom-designed spatial visualization gaming application over a six-week training protocol consisting of tasks and procedures that varied in task load and spatial characteristics. Findings revealed significant small (Cohen's d: 0.10) to large (Cohen's d: 2.40) effects of task load and changes in the spatial characteristics of the task, such as orientation or position changes, on time spent and oxygenated hemoglobin (HbO) measures from all the prefrontal cortex (PFC) areas. Transfer had a large (d = 1.37) significant effect on time spent and HbO measures from right anterior medial PFC (AMPFC); while training had a moderate (d = 0.48) significant effect on time spent and HbR measures from left AMPFC. The findings from this study have important implications for VR training, research, and instructional design focusing on enhancing the learning, retention, and transfer of spatial skills within and across various VR-based training scenarios.

Exploring the Effects of Interruptions in Different Phases of Complex Decision-Making Tasks

OBJECTIVE

The study aims to examine the effects of interruptions in major phases (i.e., problem-identification, alternative-development, and evaluation-and-selection) of complex decision-making tasks.

BACKGROUND

The ability to make complex decisions is of increasing importance in workplaces. Complex decision-making involves a multistage process and is likely to be interrupted, given the ubiquitous prevalence of interruptions in workplaces today.

METHOD

Sixty participants were recruited for the experiment to complete a procurement task, which required them to define goals, search for alternatives, and consider multiple attributes of alternatives to make decisions. Participants in the three experimental conditions were interrupted to respond to messages during one of these three phases, whereas participants in the control condition were not interrupted. The impacts of interruptions on performance, mental workload, and emotional states were measured through a combination of behavioral, physiological, and subjective evaluations.

RESULTS

Only participants who were interrupted in the evaluation-and-selection phase exhibited poorer task performance, despite their positive feelings toward interruptions and confidence. Participants who were interrupted in the problem-identification phase reported higher mental workload and more negative perceptions toward interruptions. Interruptions in the alternative-development phase led to more temporal changes in arousal and valence than interruptions in other phases.

CONCLUSION

Interruptions during the evaluation-and-selection phase undermine overall performance, and there is a discrepancy between behavioral outcomes and subjective perceptions of interruption effects.

APPLICATION

Interruptions should be avoided in the evaluation-and-selection phase in complex decision-making. This phase information can be either provided by users or inferred from coarse-grained interaction activities with decision-making information systems.

Stress and Workload Assessment in Aviation-A Narrative Review

In aviation, any detail can have massive consequences. Among the potential sources of failure, human error is still the most troublesome to handle. Therefore, research concerning the management of mental workload, attention, and stress is of special interest in aviation. Recognizing conditions in which a pilot is over-challenged or cannot act lucidly could avoid serious outcomes. Furthermore, knowing in depth a pilot's neurophysiological and cognitive-behavioral responses could allow for the optimization of equipment and procedures to minimize risk and increase safety. In addition, it could translate into a general enhancement of both the physical and mental well-being of pilots, producing a healthier and more ergonomic work environment. This review brings together literature on the study of stress and workload in the specific case of pilots of both civil and military aircraft. The most common approaches for studying these phenomena in the avionic context are explored in this review, with a focus on objective methodologies (e.g., the collection and analysis of neurophysiological signals). This review aims to identify the pros, cons, and applicability of the various approaches, to enable the design of an optimal protocol for a comprehensive study of these issues.

Pilots' mental workload variation when taking a risk in a flight scenario: a study based on flight simulator experiments

Pilots' operation behavior in flight is associated with their mental state variables such as workload, situation awareness, stress, etc. The objective of this study was to investigate the dynamic process of mental workload for pilots who perform a risky flight task in simulated scenarios. Two empirical experiments were conducted to address this issue. In experiment one, 19 trainee pilots divided into high-risk and low-risk groups performed a target-search task in a low-altitude visual flight. The results showed a statistically significant interaction between groups and segments for heart rate variability (HRV). The same pattern of physiological results was replicated among participants in experiment two, in which 19 airline pilots completed an approach with low visibility. These findings highlighted the relationship between mental workload variation and risk-taking behavior, which could be considered in improving pilot selection and training to improve flight safety.

A comparative assessment of subjective experience in simulator and on-road driving under normal and time pressure driving conditions

This study conducts a comparative assessment of subjective experience of real-world and simulated world driving for investigating factors leading to simulator sickness. Thirty professional car drivers drove a fixed-base driving simulator in real and simulated worlds under No Time Pressure (NTP) and Time Pressure (TP) driving conditions. Drivers rated their perceptions based on real-world driving and simulator driving experiences after each driving session with respect to three factors: simulator sickness, mental workload, and sense of presence. The structural equation model results revealed that drivers experienced high mental workload due to TP driving conditions (factor loading = 0.90) and repeated exposure to simulated world (factor loading = 0.20) which induced simulator sickness (factor loading = 0.41) and resulted in low sense of presence (factor loading = -0.18). Thus, it can be concluded that lack of experience with virtual reality induced high simulator sickness, increased mental workload, and low sense of presence.

Performance, Hemodynamics, and Stress in a Two-Day Vigilance Task: Practical and Theoretical Implications

OBJECTIVE

To explore vigilance task performance, cerebral blood flow velocity (CBFV), workload, and stress in a within-subjects, two-session experiment.

BACKGROUND

Vigilance, or sustained attention, tasks are often characterized by a decline in operator performance and CBFV with time on task, and high workload and stress. Though performance is known to improve with practice, past research has not included measures of CBFV, stress, and workload in a within-subjects multi-session design, which may also provide insight into ongoing theoretical debate.

METHOD

Participants performed a vigilance task on two separate occasions. Performance, CBFV, workload, and self-reported stress were measured.

RESULTS

Within each session, results were consistent with the vigilance profile found in prior research. Across sessions, performance improved but the time on task decrement remained. Mean CBFV and workload ratings did not differ between sessions, but participants reported significantly less distress, worry, and engagement after session two compared to one.

CONCLUSION

Though practice may not disrupt the standard vigilance profile, it may serve to improve overall performance and reduce stress. However, repeated exposure may have negative implications for engagement and mind-wandering.

APPLICATION

It is important to better understand the relationship between experience, performance, physiological response, and self-reported stress and workload in vigilance because real-world environments often require operators to do the same task over many occasions. While performance improvement and reduced distress is an encouraging result, the decline in engagement requires further research. Results across sessions fail to provide support to the mind-wandering theory of vigilance.

A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring

The concept of vehicle automation ceases to seem futuristic with the current advancement of the automotive industry. With the introduction of conditional automated vehicles, drivers are no longer expected to focus only on driving activities but are still required to stay alert to resume control. However, fluctuations in driving demands are known to alter the driver's mental workload (MWL), which might affect the driver's vehicle take-over capabilities. Driver mental workload can be specified as the driver's capacity for information processing for task performance. This paper summarizes the literature that relates to analysing driver mental workload through various in-vehicle physiological sensors focusing on cardiovascular and respiratory measures. The review highlights the type of study, hardware, method of analysis, test variable, and results of studies that have used physiological indices for MWL analysis in the automotive context.

Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training

Training in healthcare skills can be affected by trainees' workload when completing a task. Due to cognitive processing demands being negatively correlated to clinical performance, assessing mental workload through objective measures is crucial. This study aimed to investigate task-evoked changes in pupil size as reliable markers of mental workload and clinical performance. A sample of 49 nursing students participated in a cardiac arrest simulation-based practice. Measurements of cognitive demands (NASA-Task Load Index), physiological parameters (blood pressure, oxygen saturation, and heart rate), and pupil responses (minimum, maximum, and difference diameters) throughout revealed statistically significant differences according to performance scores. The analysis of a multiple regression model produced a statistically significant pattern between pupil diameter differences and heart rate, systolic blood pressure, workload, and performance (R² = 0.280; F (6, 41) = 2.660; p < 0.028; d = 2.042). Findings suggest that pupil variations are promising markers to complement physiological metrics for predicting mental workload and clinical performance in medical practice.

Examining the Effect of Interruptions at Different Breakpoints and Frequencies Within a Task

OBJECTIVE

The purpose was to explore how event segmentation theory (EST) can be used to determine optimal moments for an interruption relying on hierarchical task analysis (HTA) to identify coarse and fine event boundaries.

BACKGROUND

Research on the effects of interruptions shows that they can be either disruptive or beneficial, depending on which aspects of an interruption are manipulated. Two important aspects that contribute to these conflicting results concern when and how often interruptions occur.

METHOD

Undergraduates completed a trip planning task divided into three subtasks. The within-subjects factor was interruption timing with three levels: none, coarse breakpoints, and fine breakpoints. The between-subjects factor was interruption frequency with two levels: one and three. The dependent measures included resumption lag, number of errors, mental workload, and frustration.

RESULTS

Participants took longer to resume the primary task and reported higher mental workload when interruptions occurred at fine breakpoints. The effect of interruptions at coarse breakpoints was similar to completing the task without interruption. Interruption frequency had no effect on performance; however, participants spent significantly longer attending to interruptions in the initial task, and within a task, the first and second interruptions were attended to significantly longer than the third interruption.

CONCLUSION

The disruptiveness of an interruption is tied to the point within the task hierarchy where it occurs.

APPLICATION

The performance cost associated with interruptions must be considered within the task structure. Interruptions occurring at coarse breakpoints may not be disruptive or have a negative effect on mental workload.

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

BACKGROUND AND OBJECTIVE

Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs).

METHODS

Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis.

RESULTS

MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively).

CONCLUSIONS

The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.