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Introduction: We developed a low‐cost, user‐friendly complementary research tool to evaluate autonomic nervous system (ANS) activity at varying levels of cognitive workload. This was achieved using visual stimuli as cognitive tasks, administered through a specially designed computer‐based test battery. Methods: To assess sympathetic stress responses, skin conductance response (SCR) was measured, and electrocardiograms (ECG) were recorded to evaluate heart rate variability (HRV), an indicator of cardiac vagal tone. Twenty‐five healthy adults participated in the study. SCR and ECG recordings were made during both tonic and phasic phases using a computer‐based system designed for visual stimuli. Participants performed a button‐pressing task upon seeing the target stimulus, and the relationship between reaction time (RT) and cognitive load was evaluated. Results: Analysis of the data showed higher skin conductance levels (SCLs) during tasks compared to baseline, indicating successful elicitation of sympathetic responses. RTs differed significantly between simple and cognitive tasks, increasing with mental load. Additionally, significant changes in vagally mediated HRV parameters during tasks compared to baseline highlighted the impact of cognitive load on the parasympathetic branch of the ANS, thereby influencing the brain–heart connection. Conclusion: Our findings indicate that the developed research tool can successfully induce cognitive load, significantly affecting SCL, RTs, and HRV. This validates the tool's effectiveness in evaluating ANS responses to cognitive tasks. [ABSTRACT FROM AUTHOR]

Nowadays, with the remarkable advancements in detection instruments and artificial intelligence, there has been extensive utilization of human mental state monitoring in various domains. Few studies have explored how nonlinear analysis methods can detect cognitive workload despite the complex nature of EEG signals and advancements in signal processing techniques. In addition, the fuzziness of human mental conditions makes the need to use fuzzy engineering tools tangible in this field. Therefore, this investigation aimed to develop a decision support algorithm to improve previous efforts for the classification of task EEG and resting through machine learning algorithms. Various nonlinear features were calculated from all 19 EEG channels: Hurst exponent, Lempel–Ziv complexity, detrended fluctuation analysis, Higuchi fractal dimension, Katz fractal dimension, permutation entropy, singular value decomposition entropy, Petrosian fractal dimension, sample entropy, and Lyapunov exponent. During the classification step, a newly developed EPC-FC (Expert per Class Fuzzy Classifier) is introduced, utilizing an ensemble framework with specialized sub-classifiers for identifying a particular condition. By training sub-classifiers with the negative correlation learning (NCL) approach, the EPC-FC is designed to be exceptionally adaptable. Additionally, the separation of sub-classifiers within each class provides versatility and clarity to the system’s design. The proposed approach based on fuzzy systems and nonlinear analyses was applied to EEG data for mental workload recognition, which provides an excellent accuracy of 98.50% and an F1-score of 98.56% which is much higher than previous findings in this field. Also, the obtained results indicate that utilizing the proposed EPC-FC classifier maintains a consistently high accuracy exceeding 90% across various levels of SNRs. The obtained results proved the high potential of nonlinear analysis to detect cognitive states of the brain, which is consistent with the nonlinear and fuzzy nature of EEG data. Other nonlinear approaches should be considered for future studies to improve the current results. [ABSTRACT FROM AUTHOR]

This study aimed to perform a comparative study of the functional connectivity of different frequency bands for the identification of resting and arithmetic cognitive workload EEG using machine learning techniques. Functional connectivity was calculated from preprocessed EEGs for both rest and task states in 5 EEG sub-bands: alpha (8–13 Hz), theta (4–8 Hz), delta (1–4 Hz), gamma (30–45 Hz), and beta (13–30 Hz). This was done through Weighted Phase Lag Index (WPLI). After that, PCA was applied to the calculated feature vectors to decrease the dimensionality of the feature space. Eventually, the normalized chosen features were used as input for different machine learning-based classification models, and the performance was assessed through the leave-one-subject cross-validation (LOSOCV) algorithm. Experimental results showed that the classification results on the basis of the connectivity features of delta, theta, alpha, beta, and gamma frequency bands were 90.27%, 77.78%, 62.50%, 62.50%, and 76.39%, respectively. The obtained results showed that used machine learning models and functional connectivity technique are successfully applied to detect mental workload from rest- and task-EEG. In summary, EEG functional connectivity in the delta frequency is a potent tool for comprehending the neural basis of mental workload and has significant applications in various fields. [ABSTRACT FROM AUTHOR]

Estimating cognitive workload levels is an emerging research topic in the cognitive neuroscience domain, as participants' performance is highly influenced by cognitive overload or underload results. Different physiological measures such as Electroencephalography (EEG), Functional Magnetic Resonance Imaging, Functional near-infrared spectroscopy, respiratory activity, and eye activity are efficiently used to estimate workload levels with the help of machine learning or deep learning techniques. Some reviews focus only on EEG-based workload estimation using machine learning classifiers or multimodal fusion of different physiological measures for workload estimation. However, a detailed analysis of all physiological measures for estimating cognitive workload levels still needs to be discovered. Thus, this survey highlights the in-depth analysis of all the physiological measures for assessing cognitive workload. This survey emphasizes the basics of cognitive workload, open-access datasets, the experimental paradigm of cognitive tasks, and different measures for estimating workload levels. Lastly, we emphasize the significant findings from this review and identify the open challenges. In addition, we also specify future scopes for researchers to overcome those challenges. [ABSTRACT FROM AUTHOR]

The reliance on vision to control a myoelectric prosthesis is cognitively burdensome and contributes to device abandonment. The feeling of uncertainty when gripping an object is thought to be the cause of this overreliance on vision in hand-related actions. We explored if experimentally reducing grip uncertainty alters the visuomotor control and mental workload experienced during initial prosthesis use. In a repeated measures design, twenty-one able-bodied participants took part in a pouring task across three conditions: (a) using their anatomical hand, (b) using a myoelectric prosthetic hand simulator, and (c) using a myoelectric prosthetic hand simulator with Velcro attached to reduce grip uncertainty. Performance, gaze behaviour (using mobile eye-tracking) and self-reported mental workload, was measured. Results showed that using a prosthesis (with or without Velcro) slowed task performance, impaired typical eye-hand coordination and increased mental workload compared to anatomic hand control. However, when using the prosthesis with Velcro, participants displayed better prosthesis control, more effective eye-hand coordination and reduced mental workload compared to when using the prosthesis without Velcro. These positive results indicate that reducing grip uncertainty could be a useful tool for encouraging more effective prosthesis control strategies in the early stages of prosthetic hand learning. [ABSTRACT FROM AUTHOR]

This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios. [ABSTRACT FROM AUTHOR]

To improve the interaction between drivers and the in‐vehicle information system (IVIS), various intelligent agents, such as robot agents, virtual agents, and voice‐only agents, have been integrated into vehicles. However, it is not yet clear which type of in‐vehicle agent is best suited to the driving context. This study aims to investigate the effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload. In a within‐subject simulated driving experiment, 22 participants interacted with three different in‐vehicle agents (smartphone agent, robot agent, and virtual agent). Functional near‐infrared spectroscopy and electrocardiogram (ECG) were used to record prefrontal cortex activation and electrical changes associated with cardiac activity during simulated driving, respectively. The results show that the smartphone agent had the lowest perceived usability scores, oxygenated hemoglobin concentration variation (ΔHbO), and maximum ECG signal variation compared to baseline. There were no statistical differences in cognitive workload, perceived usability scores, brain area activation, and ECG signals between the robot agent and the virtual agent. The research findings demonstrate the positive effects of the anthropomorphic appearance of in‐vehicle agents on perceived usability and contribute to improving the design of in‐vehicle intelligent agents. [ABSTRACT FROM AUTHOR]

AbstractThis study examines the impact of visual assistance and cognitive load on situation awareness (SA) during takeover events in Level 3 (L3) autonomous driving, where drivers are permitted to engage in Non-Driving Related Tasks (NDRTs). Utilizing a driving simulator, the research explores how different NDRTs, occupying various sensory channels, influence drivers’ SA under two visual assistance conditions: full marking and key marking. Results from the Situation Awareness Global Assessment Technique (SAGAT) and NASA Task Load Index (NASA-TLX) scales, along with eye-tracking data, demonstrate significant interactions between visual assistance conditions and tasks, highlighting key marking’s superior effectiveness in enhancing SA across all NDRTs. The study reveals that cognitive workload, largely arising from NDRTs rather than road attention, remains unaffected by visual assistance conditions. Findings suggest that drivers’ strategies for allocating attention are consistent, regardless of the visual task presence. This research contributes to understanding SA in autonomous driving takeovers, offering valuable insights for developing Human-Machine Interface (HMI) designs to improve SA and takeover performance in autonomous driving systems. [ABSTRACT FROM AUTHOR]

Dysregulation of the autonomic nervous system (ANS) is commonly observed in various mental disorders, particularly when individuals engage in prolonged cognitive‐emotional tasks that require ANS adjustment to workload. Although the understanding of the temporal dynamics of sympathetic and parasympathetic tones in obsessive‐compulsive disorder (OCD) is limited, analyzing ANS reactions to cognitive‐emotional workload could provide valuable insights into one of the underlying causes of OCD. This study investigated the temporal dynamics of heart rate (HR) and pupil area (PA) while participants with OCD and healthy volunteers solved antisaccade tasks, with affective pictures serving as central fixation stimuli. The data of 31 individuals with OCD and 30 healthy volunteers were included in the study, comprising three separate blocks, each lasting approximately 8 min. The results revealed an increase in sympathetic tone in the OCD group, with the most noticeable rise occurring during the middle part of each block, particularly during the presentation of negative stimuli. Healthy volunteers demonstrated adaptive temporal dynamics of HR and PA from the first block to the last block of tasks, whereas individuals with OCD exhibited fewer changes over time, suggesting a reduced adaptation of the ANS sympathetic tone to cognitive‐emotional workload in OCD. Altered autonomic nervous system responses in patients with OCD could potentially underlie the causes of obsessive‐compulsive disorder; however, they have not been sufficiently investigated at present. By examining the temporal dynamics of pupil area and heart rate, we gain deeper insights into how individuals with obsessive‐compulsive disorder react to cognitive‐emotional stimuli. [ABSTRACT FROM AUTHOR]

Controlling assistive robots can be challenging for some users, especially those lacking relevant experience. Augmented Reality (AR) User Interfaces (UIs) have the potential to facilitate this task. Although extensive research regarding legged manipulators exists, comparatively little is on their UIs. Most existing UIs leverage traditional control interfaces such as joysticks, Hand-Held (HH) controllers and 2D UIs. These interfaces not only risk being unintuitive, thus discouraging interaction with the robot partner, but also draw the operator's focus away from the task and towards the UI. This shift in attention raises additional safety concerns, particularly in potentially hazardous environments where legged manipulators are frequently deployed. Moreover, traditional interfaces limit the operators' availability to use their hands for other tasks. Towards overcoming these limitations, in this article, we provide a user study comparing an AR Head-Mounted Display (HMD) UI we developed for controlling a legged manipulator against off-the-shelf control methods for such robots. This user study involved 27 participants and 135 trials, from which we gathered over 405 completed questionnaires. These trials involved multiple navigation and manipulation tasks with varying difficulty levels using a Boston Dynamics's Spot, a 7 df Kinova robot arm and a Robotiq 2F-85 gripper that we integrated into a legged manipulator. We made the comparison between UIs across multiple dimensions relevant to a successful human–robot interaction. These dimensions include cognitive workload, technology acceptance, fluency, system usability, immersion and trust. Our study employed a factorial experimental design with participants undergoing five different conditions, generating longitudinal data. Due to potential unknown distributions and outliers in such data, using parametric methods for its analysis is questionable, and while non-parametric alternatives exist, they may lead to reduced statistical power. Therefore, to analyse the data that resulted from our experiment, we chose Bayesian data analysis as an effective alternative to address these limitations. Our results show that AR UIs can outpace HH-based control methods and reduce the cognitive requirements when designers include hands-free interactions and cognitive offloading principles into the UI. Furthermore, the use of the AR UI together with our cognitive offloading feature resulted in higher usability scores and significantly higher fluency and Technology Acceptance Model scores. Regarding immersion, our results revealed that the response values for the AR Immersion questionnaire associated with the AR UI are significantly higher than those associated with the HH UI, regardless of the main interaction method with the former, i.e., hand gestures or cognitive offloading. Derived from the participants' qualitative answers, we believe this is due to a combination of factors, of which the most important is the free use of the hands when using the HMD, as well as the ability to see the real environment without the need to divert their attention to the UI. Regarding trust, our findings did not display discernible differences in reported trust scores across UI options. However, during the manipulation phase of our user study, where participants were given the choice to select their preferred UI, they consistently reported higher levels of trust compared to the navigation category. Moreover, there was a drastic change in the percentage of participants that selected the AR UI for completing this manipulation stage after incorporating the cognitive offloading feature. Thus, trust seems to have mediated the use and non-use of the UIs in a dimension different from the ones considered in our study, i.e., delegation and reliance. Therefore, our AR HMD UI for the control of legged manipulators was found to improve human–robot interaction across several relevant dimensions, underscoring the critical role of UI design in the effective and trustworthy utilisation of robotic systems. [ABSTRACT FROM AUTHOR]

Despite the substantial literature and human factors guidance, evaluators report challenges in selecting cognitive workload measures for the evaluation of complex human–technology systems. A review of 32 articles found that self-report measures and secondary tasks were systematically sensitive to human–system interface conditions and correlated with physiological measures. Therefore, including a self-report measure of cognitive workload is recommended when evaluating human–system interfaces. Physiological measures were mainly used in method studies, and future research must demonstrate the utility of these measures for human–system evaluation in complex work settings. However, indexes of physiological measures showed promise for cognitive workload assessment. The review revealed a limited focus on the measurement of excessive cognitive workload, although this is a key topic in nuclear process control. To support human–system evaluation of adequate cognitive workload, future research on behavioural measures may be useful in the identification and analysis of underload and overload. This review provides background for the selection of cognitive workload measures for the evaluation of complex human–technology systems and identifies future research needs for applied cognitive workload assessment. [ABSTRACT FROM AUTHOR]

ABSTRACT Introduction We developed a low‐cost, user‐friendly complementary research tool to evaluate autonomic nervous system (ANS) activity at varying levels of cognitive workload. This was achieved using visual stimuli as cognitive tasks, administered through a specially designed computer‐based test battery. Methods To assess sympathetic stress responses, skin conductance response (SCR) was measured, and electrocardiograms (ECG) were recorded to evaluate heart rate variability (HRV), an indicator of cardiac vagal tone. Twenty‐five healthy adults participated in the study. SCR and ECG recordings were made during both tonic and phasic phases using a computer‐based system designed for visual stimuli. Participants performed a button‐pressing task upon seeing the target stimulus, and the relationship between reaction time (RT) and cognitive load was evaluated. Results Analysis of the data showed higher skin conductance levels (SCLs) during tasks compared to baseline, indicating successful elicitation of sympathetic responses. RTs differed significantly between simple and cognitive tasks, increasing with mental load. Additionally, significant changes in vagally mediated HRV parameters during tasks compared to baseline highlighted the impact of cognitive load on the parasympathetic branch of the ANS, thereby influencing the brain–heart connection. Conclusion Our findings indicate that the developed research tool can successfully induce cognitive load, significantly affecting SCL, RTs, and HRV. This validates the tool's effectiveness in evaluating ANS responses to cognitive tasks.

Critical care injuries, such as burn trauma, require specialized skillsets and knowledge. A clinical decision support system to aid clinicians in providing burn patient management can increase proficiency and provide knowledge content for specific interventions. In austere environments, decision support tools can be used to aid in decision making and task guidance when skilled personnel or resources are limited. Therefore, we developed a novel software system that utilizes augmented reality (AR) capabilities to provide enhanced step-by-step instructions based on best practices for managing burn patients. To better understand how new technologies, such as AR, can be used for burn care management, we developed a burn care application for use on a heads-up display. We developed four sub-set applications for documenting and conducting burn wound mapping, fluid resuscitation, medication calculations, and an escharotomy. After development, we conducted a usability study utilizing the System Usability Scale, pre- and post- simulation surveys, and after-action reviews to evaluate the AR-based software application in a simulation scenario. Results of the study indicate that the decision support tool has generalized usability and subjects were able to use the software as intended. Here we present the first use case of a comprehensive burn management system utilizing augmented reality capabilities to deliver care.

Abstract Background Performing a surgical task subjects the surgeon to multitudinal stressors, especially with the newer 3D technology. The quantum of cognitive workload using this modern surgical system in comparison to the Conventional microscope system remains unexplored. We evaluate the surgeon’s cognitive workload and the surgical outcomes of macular hole(MH) surgery performed on a 3D versus a Conventional microscope operating system. Methods 50 eyes of 50 patients with MH undergoing surgery using the 3D or Conventional microscope visualization system. Cognitive workload assessment was done by real-time tools(Surgeons’ heart rate [HR] and oxygen saturation[SPO2]) and self-report tool(Surgery Task Load Index[SURG-TLX] questionnaire) of three Vitreoretinal surgeons. Based on the SURG-TLX questionnaire, an assessment of the workload was performed. Results Of the 50 eyes, 30 eyes and 20 eyes underwent surgery with the Conventional microscope and the 3D system, respectively. No difference was noted in the MH basal-diameter(p = 0.128), total surgical-duration(p = 0.299), internal-limiting membrane(ILM) peel time(p = 0.682), and the final visual acuity (VA; p = 0.515) between the two groups. Both groups showed significant improvement in VA(p

The recent pandemic and associated hybrid work culture reignites the importance of extended reality (XR) technologies for remote collaboration across the world. As different ranges of automation are introduced in the industry with an increasing focus on digitalization, safety, and productivity, it is important to understand the context and physiological metrics of existing human operators. This paper describes the implementation of a virtual reality (VR) and mixed reality (MR) interface of a welding process and compares operators' performance using such technologies. Initially, MR and VR were compared with respect to a pointing task followed by a welding task involving trajectory definition and actual robotic arm movement. A plethora of parameters involving ocular data, Electroencephalography (EEG), hand movement, subjective opinion and quantitative measures were recorded and analyzed. The results of physiological parameters such as EEG based Task Load Index, Task Engagement Index, ocular fixation rate and average fixation duration indicated that the VR interaction involved higher levels of engagement, lower mental processing load and distinct visual processing mechanisms in the optical cortex compared to the MR interaction. Similar comparison trends observed in these parameters across both tasks confirmed the reproducibility of the experiment methodology and results. Results from the study can be used in terms of selecting rendering media for other immersive applications such as teleoperation, path planning and navigation scenarios in multiple domains such as manufacturing, robotics, healthcare, and education. The results were used to develop a multi-modal VR interface with a novel collision-based weld path definition method suitable for industrial deployment. [ABSTRACT FROM AUTHOR]

The eye tracking approach has the potential to facilitate more tailored and intelligent geoapplication interactions. There is a lack of knowledge regarding how gaze interaction influences user performance (accuracy and efficiency), visual behavior, and cognitive workload in real-world map-reading settings. In this study, we adopted a low-cost eye tracker and dwell time method to implement a fundamental gaze-based map interaction: target (point, line and area) selection. We administered a comparative experiment in which 36 participants were asked to perform a series of map target selection tasks using gaze-based and mouse interactions. The results demonstrated that gaze-based map interaction is comparable to mouse in terms of overall efficiency. By evaluating the visual behavior of the participants, we found that mouse interaction exhibited heightened fixation frequency and larger saccade amplitude than gaze interaction. Although gaze-based map interaction frustrated participants more often, the overall cognitive workload was no different from that of mouse interaction. These differences between interaction modes were largely affected by task types but rarely dependent on target shapes or positions. Moreover, participants evaluated gaze interaction higher in terms of attractiveness, stimulation and novelty. These results are beneficial for map interaction researchers and developers. [ABSTRACT FROM AUTHOR]

AbstractWith the increasing popularity and unparalleled ubiquity of smartphones, researchers in various fields are designing and developing novel applications for this platform as part of the solution to the challenging problems they face. Usability lies at the core of the user experience of such applications. However, the assessments used are often limited to summative and post-event methods, which can overlook subtle yet impactful issues. Objective and instantaneous measures of cognitive workloads provide a solution to this shortcoming. Our previous research has established the reliability of maximum pupil dilation, measured with Tobii Pro Nano, as a preeminent indicator of cognitive workload surges in mobile application users. In this study, we used this measure to locate user cognitive workload peaks while using Conflict Solver and discovered subtle user interface issues that were not reported in the post-usability interview. A total of 30 participants completed a Conflict Solver usability experiment with two phases. In phase 1, the participants performed two “Add a Term” tasks on the original Conflict Solver, followed by a semi-structured interview about their experience with the application. A few subtle usability issues with a drop-down menu were detected through identifying user cognitive workload peaks. In phase 2, the same participants completed the same tasks on Conflict Solver with a redesigned and extended drop-down menu. The results showed that the new design solved the usability issues, and the participants became more favor the drop-down menu over the input box. In conclusion, including maximum pupil dilation into the usability assessment toolkit would provide a more objective and comprehensive usability assessment of a smartphone application. It can also be used to verify the successfulness of a user interface design solution. [ABSTRACT FROM AUTHOR]

Gaze and pupil metrics are used to represent higher cognitive processes in a variety of contexts. One growing area of research is the real-time assessment of workload and corresponding effort in gamified or simulated cognitive and motor tasks, which will be reviewed in this paper. While some measurements are consistent across studies, others vary and are likely dependent on the nature of the effort required by the task and the resulting changes in arousal. Pupil diameter is shown to consistently increase with task effort and arousal; however, the valence of arousal must be considered. In many cases, measures of pupil diameter were sensitive to both excessive and insufficient challenge. Overall, it is evident that gaze and pupil metrics are valuable to assess the cognitive state during gamified and simulated tasks, and further research is indicated regarding their use in clinical populations in rehabilitation to inform optimally engaging interventions. [ABSTRACT FROM AUTHOR]

This article examines the user experience and cognitive workload in digital storytelling through immersive virtual reality (VR). Specifically, it explores the relationship between various aspects of the user experience and their link to the cognitive workload. The investigation was conducted by means of a large-scale evaluation of an underwater archaeological VR experience that simulated diving into the reconstructions of a submerged ancient site combined with interactive 360° video storytelling. The evaluation included 125 participants from two different geographical locations. Results revealed a strong interdependence between all user experience scales, including presence, immersion, engagement, emotional response, state of flow, subjective judgment, and technological adoption of the VR equipment. Moreover, the sense of presence in VR was strongly related to the reported task performance. The article highlights the importance of understanding the user experience and cognitive workload when creating digital storytelling applications in immersive VR. [ABSTRACT FROM AUTHOR]

Critical care injuries, such as burn trauma, require specialized skillsets and knowledge. A clinical decision support system to aid clinicians in providing burn patient management can increase proficiency and provide knowledge content for specific interventions. In austere environments, decision support tools can be used to aid in decision making and task guidance when skilled personnel or resources are limited. Therefore, we developed a novel software system that utilizes augmented reality (AR) capabilities to provide enhanced step-by-step instructions based on best practices for managing burn patients. To better understand how new technologies, such as AR, can be used for burn care management, we developed a burn care application for use on a heads-up display. We developed four sub-set applications for documenting and conducting burn wound mapping, fluid resuscitation, medication calculations, and an escharotomy. After development, we conducted a usability study utilizing the System Usability Scale, pre- and post- simulation surveys, and after-action reviews to evaluate the AR-based software application in a simulation scenario. Results of the study indicate that the decision support tool has generalized usability and subjects were able to use the software as intended. Here we present the first use case of a comprehensive burn management system utilizing augmented reality capabilities to deliver care. [ABSTRACT FROM AUTHOR]

In order to solve the existing problem of the combination of traffic signs at typical grassland highway intersections, the combination setting of intersection traffic engineering facilities was optimized. Through simulation experiments, the electroencephalogram (EEG) signals of 40 drivers were collected, clustered into 5 (MS1—MS5) microstate topographic maps, and the reaction time and duration, coverage, frequency of occurrence and conversion probability of the drivers were statistically analyzed. The experimental results showed that the default network and the dorsal attention network in the driver's EEG microstate played a major role in the cognitive process of the combination of traffic facilities at the grassland highway intersection. The duration of MS4 and the conversion probability of MS1—MS3 increased with the increase of transportation facilities, which can be used as direct indicators to evaluate driver's cognitive load. Microstate indicators and reaction time trend analysis found that at the information level C, that was, the combination of four traffic engineering facilities, the driver's brain state had the best performance, the strongest cognitive ability, the smaller load and the fastest response. [ABSTRACT FROM AUTHOR]

Background: Performing a surgical task subjects the surgeon to multitudinal stressors, especially with the newer 3D technology. The quantum of cognitive workload using this modern surgical system in comparison to the Conventional microscope system remains unexplored. We evaluate the surgeon's cognitive workload and the surgical outcomes of macular hole(MH) surgery performed on a 3D versus a Conventional microscope operating system. Methods: 50 eyes of 50 patients with MH undergoing surgery using the 3D or Conventional microscope visualization system. Cognitive workload assessment was done by real-time tools(Surgeons' heart rate [HR] and oxygen saturation[SPO2]) and self-report tool(Surgery Task Load Index[SURG-TLX] questionnaire) of three Vitreoretinal surgeons. Based on the SURG-TLX questionnaire, an assessment of the workload was performed. Results: Of the 50 eyes, 30 eyes and 20 eyes underwent surgery with the Conventional microscope and the 3D system, respectively. No difference was noted in the MH basal-diameter(p = 0.128), total surgical-duration(p = 0.299), internal-limiting membrane(ILM) peel time(p = 0.682), and the final visual acuity (VA; p = 0.515) between the two groups. Both groups showed significant improvement in VA(p < 0.001) with a 90% closure rate at one-month post-surgery. Cognitive workload comparison, the intraoperative HR(p = 0.024), total workload score(P = 0.005), and temporal-demand dimension(p = 0.004) were significantly more in Conventional microscope group as compared to 3D group. In both the groups, the HR increased significantly from the baseline while performing ILM peeling and at the end. Conclusion: The surgeon's cognitive workload is markedly reduced while performing macular hole surgery with a 3D viewing system. Moreover, duration of surgery including ILM peel time, MH closure rates, and visual outcomes remains unaffected irrespective of the operating microscope system. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDs), such as Alzheimer's, Parkinson's, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject's available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in n = 17 healthy volunteers (9 males, 8 females, age 28.66 ± 8.8 years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease. [ABSTRACT FROM AUTHOR]

Spatial cognition plays a crucial role in academic achievement, particularly in science, technology, engineering, and mathematics (STEM) domains. Immersive virtual environments (VRs) have the growing potential to reduce cognitive load and improve spatial reasoning. However, traditional methods struggle to assess the mental effort required for visuospatial processes due to the difficulty in verbalizing actions and other limitations in self-reported evaluations. In this neuroergonomics study, we aimed to capture the neural activity associated with cognitive workload during visuospatial tasks and evaluate the impact of the visualization medium on visuospatial task performance. We utilized functional near-infrared spectroscopy (fNIRS) wearable neuroimaging to assess cognitive effort during spatial-reasoning-based problem-solving and compared a VR, a computer screen, and a physical real-world task presentation. Our results reveal a higher neural efficiency in the prefrontal cortex (PFC) during 3D geometry puzzles in VR settings compared to the settings in the physical world and on the computer screen. VR appears to reduce the visuospatial task load by facilitating spatial visualization and providing visual cues. This makes it a valuable tool for spatial cognition training, especially for beginners. Additionally, our multimodal approach allows for progressively increasing task complexity, maintaining a challenge throughout training. This study underscores the potential of VR in developing spatial skills and highlights the value of comparing brain data and human interaction across different training settings. [ABSTRACT FROM AUTHOR]

Cognitive ergonomics refer to mental resources and is associated with memory, sensory motor response, and perception. Cognitive workload (CWL) involves use of working memory (mental strain and effort) to complete a task. The three types of cognitive loads have been divided into intrinsic (dependent on complexity and expertise), extraneous (the presentation of tasks) and germane (the learning process) components. The effect of robotic surgery on CWL is complex because the postural, visualisation, and manipulation ergonomic benefits for the surgeon may be offset by the disadvantages associated with team separation and reduced situation awareness. Physical fatigue and workflow disruptions have a negative impact on CWL. Intraoperative CWL can be measured subjectively post hoc with the use of self-reported instruments or objectively with real-time physiological response metrics. Cognitive training can play a crucial role in the process of skill acquisition during the three stages of motor learning: from cognitive to integrative and then to autonomous. Mentorship, technical practice and watching videos are the most common traditional cognitive training methods in surgery. Cognitive training can also occur with computer-based cognitive simulation, mental rehearsal, and cognitive task analysis. Assessment of cognitive skills may offer a more effective way to differentiate robotic expertise level than automated performance (tool-based) metrics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cognitive workload analysis is an important aspect of safety studies at the Spanish Air Force Academy where students must complete a dual academic curriculum based on military pilot training combined with an industrial engineering degree. Recently, a mental workload assessment (MWA) and forecasting model based on Shannon's law from information theory (IT) has been published; it proposes a new mathematical procedure (MWA-IT) that defines a workload index that could be extrapolated to other case studies. The aim of this study was to adapt this model to the Spanish University Centre of Defence to calculate the mental workload caused by the listening practice in English as a foreign language. In addition, a contrasting methodology, the NASA task load index (NASA-TLX), was applied to validate the proposed model using the error study provided by SMAPE and MSE. The results established an expected reference baseline for MWA-IT in English listening that is between 36 and 92 at the end of the four courses, which corresponds to the students that start with the lowest English level (higher workload = 92) and the ones with the highest English level certification (lowest workload = 36); meanwhile, the NASA-TLX result was between 49.8 and 193.7 for the same circumstances. The main difference is that MWA-IT can be predicted with 41% less deviation than can NASA-TLX and does not require the completion of a questionnaire following the activities. Finally, the study also highlights the fact that that nearly 65% of the workload was caused by the first two courses, when the advanced STEM subjects were taught and the pilot learning and practice program had not yet begun. This methodology may help the teachers in charge to redesign or add new content depending on the expected workload reference. [ABSTRACT FROM AUTHOR]

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Workload is crucial in managing and maintaining good performance of human resources and allocations. In an advanced manufacturing industry, human job functions had shifted to cognitive tasks. Thus, cognitive workload evaluation should be used to monitor worker's workload in optimal condition. Most common tool of cognitive workload tools are perceived measurement, like NASA - TLX questionnaire. Despite of its sensitivity to capture workload felt by the workers, this subjective measurement was prone to bias. Objective measurement utilizing biometrics data of the human body during working state was useful to eliminate bias. Cardiac biometrics were one of the many that were closely related to mental activity changes. The objective of this study was to understand the relationship of cardiac biometrics to perceived workload as an indicator of cognitive workload analysis. The study utilized four biometrics, heart rate, HRV low frequency power, total frequency power and ratio of low and high frequency power, were used to analyzed a one hour long cognitive based study case. The study case was designed in a manufacturing planning context referring to manufacturing aptitude tests, to induce cognition process on 30 participants. The biometrics and NASA - TLX score result of all the participants, were then calculated as effect size standardization before input into random forest regressor model to analyze relationship between cardiac biometrics and perceived workload. The result found a moderate relationship between the two (r2 = 0.576). Features importance also showed the most impactful feature to the model is the effect size of frequency power ratio. However, it is recommended to always consider evaluating multiple cardiac biometrics in workload analysis to ensure good model performance. [ABSTRACT FROM AUTHOR]

Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services. [ABSTRACT FROM AUTHOR]

Chronic pain is experienced by 1 in 5 adults in the United States and is often accompanied by fatigue, poor sleep quality, and psychological symptoms. These negative factors related to chronic pain are also associated with increased mental workload, particularly in the workplace. Mental workload refers to the amount of cognitive effort used by an individual to complete a task(s). The present study was designed to assess the relationship between chronic pain and perceived mental workload. Participants with and without chronic pain completed 4 variations of an N-back task (used to induce perceived mental workload at increasing levels). After completing each variation of the N-back task, participants completed the NASA-Task Load Index (TLX), a validated subjective measure of perceived workload. A mixed-repeated-measures ANOVA and Bayesian mixed-repeated-measures ANOVA were computed to assess the impact of chronic pain on perceived mental workload (as evaluated by NASA-TLX). There was an observed interaction effect of workload and chronic pain, F(3, 135) = 4.72, p = .004, ή² = .33, highlighting that potential increases in workload may affect individuals with chronic pain at a greater magnitude than individuals with no chronic pain. This relationship is important to understanding and mitigating the negative symptoms of chronic pain. Future studies should be completed to investigate further the relationship between chronic pain and mental workload, including electrophysiological measures (to assess workload more deeply and correlate with cortical activities) and measures of fatigue (to assess fatigue's role in the relationship). [ABSTRACT FROM AUTHOR]

Process control room operators (PCRO) perform a range of complex cognitive safety-critical tasks. The aim of this exploratory sequential mixed methods study was to develop an occupation specific tool to measure the task load of PCRO using NASA Task Load Index (TLX) methodology. Participants were 30 human factors experts and 146 PCRO at two refinery complexes in Iran. Dimensions were developed via a cognitive task analysis, a research review, and three expert panels. Six dimensions were identified: perceptual demand, performance, mental demand, time pressure, effort, and stress. Data from 120 PCRO confirmed that the developed PCRO-TLX has acceptable psychometric properties, and a comparison with the NASA-TLX confirmed that perceptual, not physical, demand was relevant for measuring workload in PCRO. There was a positive convergence of scores of the Subjective Workload Assessment Technique and the PCRO-TLX. This reliable tool (α = 0.83) is recommended for risk assessing the task load of PCRO. Practitioner summary: There are benefits of having a specific tool to measure task load in safety critical roles. Thus, we developed and validated an easy-to-use targeted tool, the PCRO-TLX, for process control room operatives. Timely use and response will assure optimal production alongside health and safety in an organisation. Abbreviations: PCRO: process control room operator; TLX: task load index; PCRO-TLX: process control room operator task load index; NASA-TLX: National Aeronautics and Space Administration task load index; SWAT: subjective workload assessment technique; DALI: driving activity load index; SURG-TLX: surgery task load index; SIM-TLX: virtual reality simulation task load index; VACP: visual, auditory, cognitive and psychomotor; CVI: content validity index; CVR: content validity ratio; RMSEA: root mean square of error approximation; GFI: goodness of fit index; AGFI: adjusted goodness of fit index; CFI: comparative fit index; ANOVA: analysis of variance; CI: confidence interval [ABSTRACT FROM AUTHOR]

Human operators often experience large fluctuations in cognitive workload over seconds timescales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators' spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are reflected in similarly rapid fluctuations in spare capacity, and if so what aspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standard detection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiring monitoring and refueling of a fleet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect differences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators' situational awareness with respect to fuel levels were much more effective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators' cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.

This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios.

Abstract 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.