Levels Of Cognitive Workload Research Articles

Investigating Methods for Cognitive Workload Estimation for Assistive Robots.

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones.

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

An Adaptive Virtual Reality-Based Training System for Pilots

The successful operation of military aviation depends on effective pilot training. Current training capabilities of the United States Air Force might not be sufficient to meet the demand for new pilots. To help resolve this issue, this implementation study focused on developing an adaptive virtual reality (VR)-based training system for pilots. This prototype was built leveraging the three fundamental components for an adaptive training system including the trainee’s performance measures, adaptive logic, and adaptive variables. The prototype was based on the startup procedure for an F-16 cockpit and included adaptive feedback, display features, and various difficulty levels to help trainees maintain an optimal level of cognitive workload while completing their training. Implementation of adaptive VR training on a wide scale has the potential to reduce training time and cost. Future work is needed to validate the effectiveness of the approach. This prototype can assist in developing future adaptive VR-training systems.

Inter-subject cognitive workload estimation based on a cascade ensemble of multilayer autoencoders

Machine learning approaches can build a computational model to predict cognitive workload levels by using electroencephalogram (EEG) feature inputs at the same time instant. However, when the EEG signals are recorded on different people, the accuracy of such a model could be impaired due to its incapability of fitting varied statistical distributions across different individuals. To this end, we propose an individual-independent workload estimator, a cascade ensemble of multilayer autoencoders to tackle the individual difference within the EEG features. It could assess the workload levels of an unseen subject by adapting the EEG data recorded from non-overlapped existing subjects. We first construct a deep stacked denoising autoencoder to abstract EEG features from a specific individual. Its shallow weights are optimized with individual-specific geometrical information of the features. Then, to find generalizable feature properties, we introduce Q-statistics to measure the independence between base learners. Finally, a regularized extreme learning machine is used as a cascade meta-classifier to fuse and filter high-level EEG abstractions and determine workload levels. We employ databases from two different experiments to validate our approach. The proposed framework can lead to acceptable accuracy and computational complexity compared to several existing workload classifiers.

COLET: A dataset for COgnitive workLoad estimation based on eye-tracking

Background and Objective: The cognitive workload is an important component in performance psychology, ergonomics, and human factors. Publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad estimation based on Eye-Tracking dataset is presented. Methods: Forty-seven (47) individuals’ eye movements were monitored as they solved puzzles involving visual search activities of varying complexity and duration. The participants’ cognitive workload level was evaluated with the subjective test of NASA-TLX and this score is used as an annotation of the activity. Extensive data analysis was performed in order to derive eye and gaze features from low-level eye recorded metrics, and a range of machine learning models were evaluated and tested regarding the estimation of the cognitive workload level. Results: The activities induced four different levels of cognitive workload. Multi tasking and time pressure have induced a higher level of cognitive workload than the one induced by single tasking and absence of time pressure. Multi tasking had a significant effect on 17 eye features while time pressure had a significant effect on 7 eye features. Both binary and multi-class identification attempts were performed by testing a variety of well-known classifiers, resulting in encouraging results towards cognitive workload levels estimation, with up to 88% correct predictions between low and high cognitive workload. Conclusions: Machine learning analysis demonstrated potential in discriminating cognitive workload levels using only eye-tracking characteristics. The proposed dataset includes a much higher sample size and a wider spectrum of eye and gaze metrics than other similar datasets, allowing for the examination of their relations with various cognitive states.

Association Between Operating Room Noise and Team Cognitive Workload in Cardiac Surgery.

Excessive intra-operative noise in cardiac surgery has the potential to serve as source of distraction and additional cognitive workload for the surgical team, and may interfere with optimal performance. The separation from bypass phase is a technically complex phase of surgery, making it highly susceptible to communication breakdowns due to high cognitive demands and requiring tightly coupled team coordination. The objective of this study was to investigate team cognitive workload levels and communication in relation to intra-operative time periods representative of infrequent vs. frequent peaks in ambient noise. Compared to 5-minute segments with no peaks in noise at all, segments with the highest percentage of noise peaks (≥10%) were significantly associated with higher team members' heart rate before, during, and after noise segments analyzed. These noisier segments were also associated with a significantly higher level of case-irrelevant communication events. These data suggest that case-irrelevant conversations associated with a greater degree of excessive peaks in noise may be associated with team workload levels, warranting further investigation into efforts to standardize communication during critical surgical phases.

Assortment levels, pupillary response, and product preference

ABSTRACT Nowadays, many online retailers allow consumers to flexibly change the assortment size in a product display. Therefore, assessing the impact of varied assortment levels on consumers’ cognitive process and decision outcome is of great interest to marketers. Using consumers’ pupil size as a biological indicator of their cognitive process during decision-making, we empirically examine how varied assortment levels affect consumers’ product preferences through their cognitive responses. Our results show that a larger assortment size prompts consumers’ pupils to constrict, indicating a lower level of cognitive workload. Consumers’ pupil size moves in the same direction with the price levels of the chosen option and the opposite direction with the familiarity of the chosen option. The mediation test demonstrates that the relationship between the assortment levels and product preference is statistically mediated by consumers’ pupillary response during decision-making.

MP10-07 THE RELATIONSHIP OF TECHNICAL SKILLS AND COGNITIVE WORKLOAD TO ERRORS DURING ROBOTIC SURGICAL EXERCISES

You have accessJournal of UrologyCME1 May 2022MP10-07 THE RELATIONSHIP OF TECHNICAL SKILLS AND COGNITIVE WORKLOAD TO ERRORS DURING ROBOTIC SURGICAL EXERCISES Sidney I. Roberts, Steven Cen, Jessica H. Nguyen, Laura C. Perez, Luis G. Medina, Runzhuo Ma, Sandra P. Marshall, Rafal Kocielnik, Anima Anandkumar, and Andrew J. Hung Sidney I. RobertsSidney I. Roberts More articles by this author , Steven CenSteven Cen More articles by this author , Jessica H. NguyenJessica H. Nguyen More articles by this author , Laura C. PerezLaura C. Perez More articles by this author , Luis G. MedinaLuis G. Medina More articles by this author , Runzhuo MaRunzhuo Ma More articles by this author , Sandra P. MarshallSandra P. Marshall More articles by this author , Rafal KocielnikRafal Kocielnik More articles by this author , Anima AnandkumarAnima Anandkumar More articles by this author , and Andrew J. HungAndrew J. Hung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002532.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Surgical performance and skill in robotic surgery has been shown to impact patient outcomes and complication rates. Cognitive workload, or mental strain in the working memory, has been shown to differ between surgeon experience levels. Herein, we attempt to understand the relationship between surgeon technical skills, cognitive workload, and discrete errors committed during a simulated robotic dissection task. METHODS: Participant surgeons performed a robotic surgery dissection exercise (peeling a clementine, removing a single wedge). Participants were grouped based on surgical experience: novice (no prior surgical experience), intermediate (<100 robotic cases), and expert (≥100 cases). Technical skills were evaluated utilizing the validated Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The task was also evaluated for errors during active dissection or passive retraction. Cognitive workload was quantified as an Index of Cognitive Activity (ICA), derived from Task-Evoked-Pupillary-Response metrics; ICA ranged 0-1, with 1 representing maximum ICA. Generalized Estimating Equation (GEE) was used for all modelings to establish relationships between surgeon technical skills, cognitive workload and errors. RESULTS: Overall there were 22 patients: 7 novices, 9 intermediates, and 6 experts. We found a strong association between technical skills, as measured by multiple GEARS domains (depth perception, force sensitivity and robotic control), and passive errors - with higher GEARS scores associated with a lower relative risk of errors (all p <0.01). For novice surgeons, as average GEARS scores increased, the average estimated ICA decreased. In contrast, as average GEARS increased for expert surgeons, the average estimated ICA increased. When exhibiting optimal technical skill (maximal GEARS scores) novices and experts had a similar range of ICA scores (ICA 0.47 and 0.42, respectively) (Figure 1). CONCLUSIONS: This study found that there is an optimal cognitive workload level for surgeons of all experience levels during our robotic surgical exercise. Select technical skills were strong predictors of errors. Future research will explore whether an ideal cognitive workload range truly optimizes surgical performance and reduce surgical errors. Source of Funding: This study was supported in part by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number K23EB026493 © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e148 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sidney I. Roberts More articles by this author Steven Cen More articles by this author Jessica H. Nguyen More articles by this author Laura C. Perez More articles by this author Luis G. Medina More articles by this author Runzhuo Ma More articles by this author Sandra P. Marshall More articles by this author Rafal Kocielnik More articles by this author Anima Anandkumar More articles by this author Andrew J. Hung More articles by this author Expand All Advertisement PDF DownloadLoading ...

Automated Classification of Cognitive Workload Levels Based on Psychophysiological and Behavioural Variables of Ex-Gaussian Distributional Features.

The study is focused on applying ex-Gaussian parameters of eye-tracking and cognitive measures in the classification process of cognitive workload level. A computerised version of the digit symbol substitution test has been developed in order to perform the case study. The dataset applied in the study is a collection of variables related to eye-tracking: saccades, fixations and blinks, as well as test-related variables including response time and correct response number. The application of ex-Gaussian modelling to all collected data was beneficial in the context of detection of dissimilarity in groups. An independent classification approach has been applied in the study. Several classical classification methods have been invoked in the process. The overall classification accuracy reached almost 96%. Furthermore, the interpretable machine learning model based on logistic regression was adapted in order to calculate the ranking of the most valuable features, which allowed us to examine their importance.

Machine learning-based approach for identifying mental workload of pilots

In general, the fighter pilots are required to engage themselves entirely during flight operations involved in air-to-air combat while in the cockpit of a fighter aircraft. The performance has to be monitored continuously by classifying their cognitive workload levels during different phases of flying. Towards this direction, an experimental study was conducted in a realistic high-fidelity flight simulator environment to classify the Pilots’ Cognitive Workload (PCWL) level. A real-time implementation of algorithms to effectively organize the PCWL during takeoff, cruise and landing phases, physiological signals such as ECG and EEG of fighter pilots are used. The classification algorithms such as Linear Discriminant Analysis (LDA) classifier, Support Vector Machine (SVM) classifier, k-Nearest Neighbour (k-NN) classifier have been employed. It has resulted that takeoff (LDA – 75%, kNN – 60% and SVM – 75%) and landing phase (LDA – 75%, kNN – 60% and SVM – 75%) was better classified by HRV features while using PCA and cruise phase was classified better using EEG features (LDA – 72.44%, kNN – 62.92% and SVM – 59.02%) when PCA feature reduction technique was adopted. Using significant features by feature selection methods (PCA, statistically significant features) have shown improved classification accuracy compared to all the features classification method. The LDA and SVM are consistent classifiers compare to the kNN classifier. This study helps to classify the PCWL level at each flying phase due to increased task.

A subjective one-item measure based on NASA-TLX to assess cognitive workload in driver-vehicle interaction

This research introduces requirements for subjective instruments that assess the cognitive workload of in-vehicle information systems (IVIS) while driving. The suitability of 11 existing subjective instruments is evaluated, but none of the instruments matches all of the proposed requirements for this specific scenario. Therefore, a new subjective one-item measure is presented. The one-item measure is based on the mental dimension of the NASA Task Load Index (NASA-TLX) questionnaire. It combines the discussed requirements for IVIS research, e.g., time-efficiency and descriptiveness for non-expert participants. A series of three studies with 107 participants overall was conducted to verify the suitability of the measure. The results show that the instrument discriminates effectively and is valid between different levels of cognitive workload. The results are stable across different experimental setups and samples, and the instrument shows high sensitivity even in non-critical cognitive load levels.

The Relationship Between Technical Skills, Cognitive Workload, and Errors During Robotic Surgical Exercises.

Purpose: We attempt to understand the relationship between surgeon technical skills, cognitive workload, and errors during a simulated robotic dissection task. Materials and Methods: Participant surgeons performed a robotic surgery dissection exercise. Participants were grouped based on surgical experience. Technical skills were evaluated utilizing the validated Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The dissection task was evaluated for errors during active dissection or passive retraction maneuvers. We quantified cognitive workload of surgeon participants as an index of cognitive activity (ICA), derived from task-evoked pupillary response metrics; ICA ranged 0 to 1, with 1 representing maximum ICA. Generalized estimating equation (GEE) was used for all modelings to establish relationships between surgeon technical skills, cognitive workload, and errors. Results: We found a strong association between technical skills as measured by multiple GEARS domains (depth perception, force sensitivity, and robotic control) and passive errors, with higher GEARS scores associated with a lower relative risk of errors (all p < 0.01). For novice surgeons, as average GEARS scores increased, the average estimated ICA decreased. In contrast, as average GEARS increased for expert surgeons, the average estimated ICA increased. When exhibiting optimal technical skill (maximal GEARS scores), novices and experts reached a similar range of ICA scores (ICA: 0.47 and 0.42, respectively). Conclusions: This study found that there is an optimal cognitive workload level for surgeons of all experience levels during our robotic surgical exercise. Select technical skill domains were strong predictors of errors. Future research will explore whether an ideal cognitive workload range truly optimizes surgical training and reduces surgical errors.

Intraoperative dynamics of workflow disruptions and surgeons' technical performance failures: insights from a simulated operating room

IntroductionFlow disruptions (FD) in the operating room (OR) have been found to adversely affect the levels of stress and cognitive workload of the surgical team. It has been concluded that frequent disruptions also lead to impaired technical performance and subsequently pose a risk to patient safety. However, respective studies are scarce. We therefore aimed to determine if surgical performance failures increase after disruptive events during a complete surgical intervention.MethodsWe set up a mixed-reality-based OR simulation study within a full-team scenario. Eleven orthopaedic surgeons performed a vertebroplasty procedure from incision to closure. Simulations were audio- and videotaped and key surgical instrument movements were automatically tracked to determine performance failures, i.e. injury of critical tissue. Flow disruptions were identified through retrospective video observation and evaluated according to duration, severity, source, and initiation. We applied a multilevel binary logistic regression model to determine the relationship between FDs and technical performance failures. For this purpose, we compared FDs in one-minute intervals before performance failures with intervals without subsequent performance failures.ResultsAverage simulation duration was 30:02 min (SD = 10:48 min). In 11 simulated cases, 114 flow disruption events were observed with a mean hourly rate of 20.4 (SD = 5.6) and substantial variation across FD sources. Overall, 53 performance failures were recorded. We observed no relationship between FDs and likelihood of immediate performance failures: Adjusted odds ratio = 1.03 (95% CI 0.46–2.30). Likewise, no evidence could be found for different source types of FDs.ConclusionOur study advances previous methodological approaches through the utilisation of a mixed-reality simulation environment, automated surgical performance assessments, and expert-rated observations of FD events. Our data do not support the common assumption that FDs adversely affect technical performance. Yet, future studies should focus on the determining factors, mechanisms, and dynamics underlying our findings.

To Drive or to Be Driven? The Impact of Autopilot, Navigation System, and Printed Maps on Driver’s Cognitive Workload and Spatial Knowledge

The technical advances in navigation systems should enhance the driving experience, supporting drivers’ spatial decision making and learning in less familiar or unfamiliar environments. Furthermore, autonomous driving systems are expected to take over navigation and driving in the near future. Yet, previous studies pointed at a still unresolved gap between environmental exploration using topographical maps and technical navigation means. Less is known about the impact of the autonomous system on the driver’s spatial learning. The present study investigates the development of spatial knowledge and cognitive workload by comparing printed maps, navigation systems, and autopilot in an unfamiliar virtual environment. Learning of a new route with printed maps was associated with a higher cognitive demand compared to the navigation system and autopilot. In contrast, driving a route by memory resulted in an increased level of cognitive workload if the route had been previously learned with the navigation system or autopilot. Way-finding performance was found to be less prone to errors when learning a route from a printed map. The exploration of the environment with the autopilot was not found to provide any compelling advantages for landmark knowledge. Our findings suggest long-term disadvantages of self-driving vehicles for spatial memory representations.

MBioTracker: Multimodal Self-Aware Bio-Monitoring Wearable System for Online Workload Detection.

Cognitive workload affects operators' performance principally in high-risk or time-demanding situations and when multitasking is required. An online cognitive workload monitoring system can provide valuable inputs to decision-making instances, such as the operator's state of mind and resulting performance. Therefore, it can allow potential adaptive support to the operator. This work presents a new design of a wearable embedded system for online cognitive workload monitoring. This new wearable system consists of, on the hardware side, a multi-channel physiological signals acquisition (respiration cycles, heart rate, skin temperature, and pulse waveform) and a low-power processing platform. Further, on the software side, our wearable embedded system includes a novel energy-aware bio-signal processing algorithm. We also use the concept of application self-awareness to enable energy-scalable embedded machine learning algorithms and methods for online subjects' cognitive workload monitoring. Our results show that this new wearable system can continuously monitor multiple bio-signals, compute their key features, and provide reliable detection of high and low cognitive workload levels with a time resolution of 1 minute and a battery lifetime of 14.58 h in our experimental conditions. It achieves a detection accuracy of 76.6% (2.6% lower than analogous offline computer-based analysis) with a sensitivity of 77.04% and a specificity of 81.75%, on a simulated drone rescue mission task. Moreover, by applying our self-aware monitoring to exploit different energy-scalable modes, we can increase battery lifetime by 51.6% (up to 22.11 hours) while incurring an insignificant accuracy loss of 1.07%.

The cognitive workload of air pistol shooters on the aiming task

Background: Pupil dilations of the eye are known to correspond to central cognitive processes. Pupil size variations have been used for many years as a window on the mind, and various pupillary features have been associated with a number of cognitive processes Aim: The aim of this study is to examine levels of cognitive workload that occur during the aiming task of air-pistol shooting athletes according to the change that takes place in the pupils. Methods: Eight right-handed skilled male shooters (4 elite and 4 novice) were recruited in this study. A total of 320 shots - 160 dry (unscored) shots and 160 live (scored) shots - were recorded with a mobile eye-tracking device. The obtained pupil data were subjected to biometric analysis. The athletes’ pupil sizes were determined with the independent samples t-test according to whether the shot made was scored or unscored and whether the athletes were elite or novice. The athletes’ pupil size values at the beginning and end of the shot were obtained using the paired samples t-test. Results: On completion of the shot, the sport shooters’ pupils dilated on average by 2.85 mm at a rate of 32.5% compared to the situation at shot onset. Moreover, novice shooters’ pupils dilated more when firing scored shots than when firing unscored shots. According to the research results, it can be said that sports shooters engage in intensive cognitive processes while aiming and trigger pulling immediately before firing the shot. Novice shooters engage in more intensive cognitive processes when firing scored shots than when firing unscored shots. Conclusion: According to the results of the research, it can be said that in the sport of air pistol shooting, giving priority to dry (unscored) shooting training can contribute positively to the development of novice athletes’ aiming task skills and cognitive load capacity. Keywords: Cognitive workload, cognitive processes, eye tracking, air pistol, shooting

Classification of cross task cognitive workload using deep recurrent network with modelling of temporal dynamics

Cognitive workload assessment is important for optimizing multitasking abilities in the current information-rich world. A few good research studies assess cross task classification where a model trained for one task can efficiently handle other tasks. Building a reliable model for the estimation of cross task cognitive workload using electroencephalogram (EEG) signal is challenging as brain dynamics differ for various activities. The present study explores the wide visual aspects for inducing two levels of cognitive workload in the two self-design tasks. The present study addresses temporal dynamics of EEG signal using time windowing, time segment smoothing, and formation of variable duration time frames. The present study computes features from the statistical, morphological and non-linear domain for variable duration segments considering four clinical sub-bands. The neighbourhood component feature selection algorithm identifies the topmost common features of both tasks. Binary classification of cross task utilizes deep structure employing bidirectional long short-term memory (BLSTM) and LSTM. The result shows that the deep recurrent neural network (RNN) achieves the highest classification accuracy of 92.8% for the cross task in comparison to the traditional classifiers. The deep RNN also outperforms the existing literature studies for cross task classification. Result also reveals a significant improvement in classification accuracy with the usage of handcrafted features when supported by proper modelling of temporal dynamics of EEG signal. The present study exploits the capability of deep recurrent structure in handling long term dependencies of non-stationary signal and proves to be very efficient.

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.

On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions.

Cognitive workload, being a quantitative measure of mental effort, draws significant interest of researchers, as it allows to monitor the state of mental fatigue. Estimation of cognitive workload becomes especially important for job positions requiring outstanding engagement and responsibility, e.g., air-traffic dispatchers, pilots, car or train drivers. Cognitive workload estimation finds its applications also in the field of education material preparation. It allows to monitor the difficulty degree for specific tasks enabling to adjust the level of education materials to typical abilities of students. In this study, we present the results of research conducted with the goal of examining the influence of various fuzzy or non-fuzzy aggregation functions upon the quality of cognitive workload estimation. Various classic machine learning models were successfully applied to the problem. The results of extensive in-depth experiments with over 2000 aggregation operators shows the applicability of the approach based on the aggregation functions. Moreover, the approach based on aggregation process allows for further improvement of classification results. A wide range of aggregation functions is considered and the results suggest that the combination of classical machine learning models and aggregation methods allows to achieve high quality of cognitive workload level recognition preserving low computational cost.

A Dynamic Hybrid Choice Model to Quantify Stress in a Simulated Driving Environment

In this article, a dynamic hybrid choice model of the driver stress arising from cognitive workload is developed and applied to a driving simulator experiment conducted with students at the American University of Beirut. A latent or unobserved variable quantifying the state stress over time is integrated with a discrete choice model of red-light violations. The driver state stress is induced by additional cognitive workload and situational factors in an urban driving context and is dependent on a time-invariant agent effect or individual trait. Driving performance (e.g., speed and acceleration) and physiological measures (e.g., heart rate) are used as indicators of the underlying state stress. The driver state stress is found to be significantly affected by road events or situations (e.g., encountering pedestrians, trucks, and traffic light), the level of cognitive workload, the individual propensity for stress, and the mere driving task (e.g., maintaining control of the car). Moreover, results show that there is a pattern of regulatory driving behavior in response to the increasing stress. The developed model is a mathematical conceptualization of the transactional model of the driver stress and can be integrated within in-vehicle systems to detect/predict the driver state and enhance safety and well-being.