Evaluation Of Mental Workload Research Articles

Mental workload evaluation using weighted phase lag index and coherence features extracted from EEG data

Electroencephalogram (EEG) represents an effective, non-invasive technology to study mental workload. However, volume conduction, a common EEG artifact, influences functional connectivity analysis of EEG data. EEG coherence has been used traditionally to investigate functional connectivity between brain areas associated with mental workload, while weighted Phase Lag Index (wPLI) is a measure that improves on coherence by reducing susceptibility to volume conduction, a common EEG artifact. The goal of this study was to compare two methods of functional connectivity analysis, wPLI and coherence, in the context of mental workload evaluation. The study involved model development for mental workload domains and comparing their performance using coherence-based features, wPLI-based features, and a combination of both. Generalized linear mixed-effects model (GLMM) with the least absolute shrinkage and selection operator (LASSO) feature selection method was used for model development. Results indicated that the model developed using a combination of both feature types demonstrated improved predictive performance across all mental workload domains, compared to models that used each feature type individually. The R2 values were 0.82 for perceived task complexity, 0.71 for distraction, 0.91 for mental demand, 0.85 for physical demand, 0.74 for situational stress, and 0.74 for temporal demand. Furthermore, task complexity and functional connectivity patterns in different brain areas were identified as significant contributors to perceived mental workload (p-value<0.05). Findings showed the potential of using EEG data for mental workload evaluation which suggests that combination of coherence and wPLI can improve the accuracy of mental workload domains prediction. Future research should aim to validate these results on larger, diverse datasets to confirm their generalizability and refine the predictive models.

Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task.

The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots. Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated. Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics. HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7). HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation. These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Evaluation of Student Mental Workload in Multicampus Lectures of Cirebon Industrial Engineering Block System

The object of this research is the Industrial Engineering (IE) study program at Bandung Institute of Technology Cirebon Campus which runs block and non-block system lecture. The block system is a lecture by combining two to three class meeting of a course in one day while non-block system is a one time meeting in a week. Block system faced a lot of complained from students because the class were too hectic, the inability of students to receive a lot of quantitative material, and the monotony of the class. This study evaluates student’s mental workload by comparing the results of experimental measurements while attending block and non-block classes. Mental workload was measured with three measuring instruments, named NASA-TLX, Karolinska Sleepiness Scale (KSS), and Stroop test. Experimental results with 38 students concluded that there is actually no significant difference in mental workload between the two systems. Factors that could cause this results to happen include previous experience in block classes, non-optimal block system instructional learning time, mental fatigue in students, and decreased attention.

Evaluation of mental workload in haptic-enabled virtual assembly training operations

Evaluation of mental workload in haptic-enabled virtual assembly training operations

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios.

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

An empirical investigation of driver’s eye-catching effect in the entrance zone of freeway tunnels: A naturalistic driving experiment

This study aims to evaluate the impact of different visual attraction environments at the entrance zone of freeway tunnels on drivers' visual performance and subjective mental workload. Through a naturalistic driving experiment conducted on actual roads, participants experienced various visual attraction environments at the entrance zones of freeway tunnels. The selected visual environments included baseline, landscape-style architecture, tip slogan, and billboard scenarios. Objective visual performance was assessed using eye-tracking devices to collect participants' eye movement data, while subjective mental workload was evaluated through the NASA-TLX measurement reports administered after the driving experiment. The results indicate that the visual attraction environments at the entrance zone of freeway tunnels have significant impacts on drivers’ visual attention level, cognitive workload, and subjective mental workload. Under the billboard condition, participants exhibited the longest first fixation duration (FFD) and paid the earliest attention to visual attraction (reflected by participants positioned their initial fixation at the visual attraction farthest from the tunnel entrance zone (DFT)). Additionally, the tip slogan condition resulted in the highest mean fixation duration (MFD), the largest percentage of total fixation duration on the visual attraction (PTFD), and the highest cognitive workload (reflected in maximum pupil diameter (PD)). Billboard at the entrance zone of freeway tunnel had the greatest effect on attracting drivers’ visual attention, while tip slogan resulted in the highest cognitive workload on drivers. The results of the subjective mental workload evaluation (NASA-TLX) also indicated that tip slogan had the greatest impact on drivers’ subjective mental workload. This study provides valuable insights into the potential impact of different visual attraction environments at the entrance zone of freeway tunnels on drivers' eye-catching effect. The results contribute to both theoretical knowledge and practical applications in the field of transportation safety.

Evaluation of the Train Drivers Mental Workload to Increase Well-being

Indonesia's logistics system is crucial for economic growth, with high costs impacting competitiveness and the economy. Transportation is the most dominant component, ensuring mobility and impacting national development. PT Angkutan Barang (PT AB)provides efficient railway transportation services. Despite a 70% drop in passenger transportation revenue during the COVID-19 pandemic, freight transportation increased by 18.3% in 2021, supporting the company's performance during a crisis. The company's increased coal transportation targets require more train drivers, for smooth operation. However, strict training and tight schedules can disrupt train driver’s fulfillment and increase the likelihood of overtime. Long working hours can cause fatigue and stress and increase the risk of accidents, impacting the company's operations and causing significant losses. Thus, train drivers play a crucial role at PT AB, and it is important to assess their mental workload to ensure their mental health using the RNASA-TLX method. The study examines the mental workload of 181 train drivers at Tanjungenim Freight Train Drivers Unit, finding an average score of 61.03, classified as "Moderate". Freight train drivers' mental workload is significantly influenced by visual, temporal, auditory, and mental demands, with schedule uncertainty and unique work environments being the most influential factors. Train driver's mental workload varies, with 52 having a low workload, affecting boredom, productivity, and human error, and 42 having a high workload, potentially requiring stress management training.

Analisis Beban Kerja di Laboratorium Produksi PT. XYZ Dengan Metode CVL dan NASA-TLX

To evaluate the workload, one can consider the combination of physical and mental effort that employees undergo. The quantification of physical workload is accomplished through the utilization of the Cardiovascular Load (%CVL) methodology, whereas the evaluation of mental workload is conducted employing the NASA-TLX methodology. Pulse measurement is employed to gather data on the physical exertion level. Concurrently, the cognitive load was alleviated by administering NASA-TLX surveys to the personnel. The physical workload measurements were acquired from a sample of 12 employees who showed no indications of fatigue. Meanwhile, the measurements of mental workload indicate a substantial level of both elevated and extremely elevated workload One employee was classified as having an extremely heavy workload, while the majority of respondents (11 out of 12) assessed their workload as high. The results suggest that around 33% of employees face a substantial mental workload, underscoring the need for a leadership policy focused on workload reduction.

Development of a Neuroergonomic Assessment for the Evaluation of Mental Workload in an Industrial Human–Robot Interaction Assembly Task: A Comparative Case Study

The disruptive deployment of collaborative robots, named cobots, in Industry 5.0 has brought attention to the safety and ergonomic aspects of industrial human–robot interaction (HRI) tasks. In particular, the study of the operator’s mental workload in HRI activities has been the research object of a new branch of ergonomics, called neuroergonomics, to improve the operator’s wellbeing and the efficiency of the system. This study shows the development of a combinative assessment for the evaluation of mental workload in a comparative analysis of two assembly task scenarios, without and with robot interaction. The evaluation of mental workload is achieved through a combination of subjective (NASA TLX) and real-time objective measurements. This latter measurement is found using an innovative electroencephalogram (EEG) device and the characterization of the cognitive workload through the brainwave power ratio β/α, defined after the pre-processing phase of EEG data. Finally, observational analyses are considered regarding the task performance of the two scenarios. The statistical analyses show how significantly the mental workload diminution and a higher level of performance, as the number of components assembled correctly by the participants, are achieved in the scenario with the robot.

Mental workload evaluation model of receiver aircraft pilots based on multiple resource theory

AbstractAerial refueling is an extender of air combat capability that has received widespread attention with the development of the military field, particularly the mental workload of the pilot performing the aerial refueling task, as it is the key to the aerial refueling success rate. Therefore, this study analyzes the behavior of receiver aircraft pilots from rendezvous to the separation phase and constructs a mental workload model to improve the aerial refueling success rate. First, the time‐channel‐action unit network was constructed based on Petri net and multiple resource theory (MRT). Second, the mental workload evaluation model was constructed from three dimensions: conventional resource occupation, additional resource occupation, and time occupation. Finally, a simulated experiment was conducted, and the results showed that the mental workload obtained by the model constructed in this study exhibits a high positive correlation with subjective mental workload, pupil diameter, and blink rate, surpassing the accuracy of the traditional MRT model.

Anticipatory information makes the difference: Behavioral effects and user assessments of a cyclist warning system to enhance cyclists’ situation awareness

Problem: Increasing numbers of crashes involving pedelecs, and particularly older pedelec users, induce a need to enhance cycling safety. We evaluated a prototype cyclist warning system (CWS) that aims to increase situation awareness (SA) by alerting to safety critical events (SCE) with trimodal (auditory, visual, tactile). Method: To investigate the effects of CWS usage, we conducted a 2x2 mixed design bicycle simulator study with factors (1) CWS usage (within: rides WITH vs. WITHOUT CWS) and (2) age group (between: younger vs. older cyclists) on braking reaction time, gaze behavior, mental workload, and perceived safety. In sum, N = 64 participants (n = 32 younger, 18–40 years; n = 32 older, ≥ 55 years) took part in the study and experienced two balanced blocks of short rides including SCE of particular relevance for cycling safety. Results: CWS usage resulted in earlier braking reactions to all investigated SCE and partly earlier fixation on the critical interaction partners (CIP) indicating increased cyclists’ SA. Consistently to behavioral measures, participants’ assessments regarding perceived safety further supported the safety improvements derived from CWS independently of age group. Moreover, CWS usage did not add to mental workload ratings. Age effects were selectively found for gaze data showing that across all SCE, older adults fixated longer and more frequently on street alignment, and less frequently on other road users. Discussion: Taken together, the CWS evaluation showed promising results indicating the potential of the tested CWS to increase SA and enhance cyclists’ safety both on a behavioral level and regarding subjective assessments. Further research should address the systems’ safety potential under real-world conditions and for situations of higher complexity. Practical Applications: Understanding the potential impact of road safety measures such as CWS is important to contribute effectively to reducing SCE.

Lower Executive Function Ability May Lead to Higher Perceived Mental Workload in Driving Scenarios

Driver mental workload may represent a mismatch between task demands and available mental resources. If driving mental workload arises from a mismatch involving executive functions, then drivers with more executive function ability should perceive less mental workload in driving situations. To test this hypothesis, 33 participants rated the mental workload associated with 16 driving scenarios and also carried out three cognitive assessment games designed to measure executive functions (response inhibition, working memory updating, switching/shifting). We found a significant relationship between two of the executive function abilities (response inhibition, and switching/shifting, but not working memory updating) and mental workload ratings. With an increase in the age of participants, we observed lower response inhibition and higher perceived mental workload after viewing representations of driving scenarios. These results demonstrate that previous results showing higher perceived mental workload for older drivers may be, at least partly, due to decreases in executive function ability as people age.

Multi-objective task allocation for collaborative robot systems with an Industry 5.0 human-centered perspective

The migration from Industry 4.0 to Industry 5.0 is becoming more relevant nowadays, with a consequent increase in interest in the operators’ wellness in their working environment. In modern industry, there are different activities that require the flexibility of human operators in performing different tasks, while some others can be performed by collaborative robots (cobots), which promote a fair division of the tasks among the resources in industrial applications. Initially, these robots were used to increase productivity, in particular in assembly systems; currently, new goals have been introduced, such as reducing operator’s fatigue, so that he/she can be more effective in the tasks that require his/her flexibility. For this purpose, a model that aims to realize a multi-objective optimization for task allocation is here proposed. It includes makespan minimization, but also the operator’s energy expenditure and average mental workload reduction. The first objective is to reach the required high productivity standards, while the latter is to realize a human-centered workplace, as required by the Industry 5.0 paradigms. A method for average mental workload evaluation in the entire assembly process and a new constraint, related to resources’ idleness, are here suggested, together with the evaluation of the methodology in a real case study. The results show that it is possible to combine all these elements finding a procedure to define the optimal task allocation that improves the performance of the systems, both for efficiency and for workers’ well-being.

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.

Cross-Task Mental Workload Recognition Based on EEG Tensor Representation and Transfer Learning.

The accurate evaluation of mental workload of operators in human machine systems is of great significance in ensuring the safety of operators and the correct execution of tasks. However, the effectiveness of EEG based cross-task mental workload evaluation are still unsatisfactory because of the different EEG response patterns in different tasks, which hindered its generalization in real scenario severely. To solve this problem, this paper proposed a feature construction method based on EEG tensor representation and transfer learning, which was verified in various task conditions. Specifically, four working memory load tasks with different types of information were designed firstly. The EEG signals of participants were collected synchronously during task execution. Then, the wavelet transform method was used to perform time-frequency analysis of multi-channel EEG signals, and three-way EEG tensor (time-frequency-channel) features were constructed. EEG tensor features from different tasks were transferred based on the criteria of feature distribution alignment and class-wise discrimination criteria. Finally, the support vector machine was used to construct a 3-class mental workload recognition model. Results showed that compared with the classical feature extraction methods, the proposed method can achieve higher accuracy in both within-task and cross-task mental workload evaluation (91.1% for within-task and 81.3% for cross-task). These results demonstrated that the EEG tensor representation and transfer learning method is feasible and effective for cross-task mental workload evaluation, which can provide theoretical basis and application reference for future researches.

Effects of Reading Text on an Optical See-Through Head-Mounted Display During Treadmill Walking in a Virtual Environment

This study evaluated the effects of reading text on an optical see-through head-mounted display during treadmill walking in a virtual environment. Thirty participants were recruited to perform Chinese reading tasks by reading six types of text layouts in two polarity settings. A virtual environment was displayed on a projection screen to simulate a walking situation in which obstacles were encountered. High- and low-density obstacles were applied. Objective measures included reading performance, gait variability, and the error count of obstacles. The subjective measures were the situational awareness rating technique (SART) and NASA-Task Load Index (NASA-TLX) scores. The results indicated that font size and polarity affected the gait variability, reading performance, situational awareness, and mental workload ratings. The obstacle density affects gait variability and situation awareness rating. The layout with 32-point text and positive polarity yielded faster reading speed, better subjective ratings, and less variability in minimum foot clearance.

Driver’s Attention Allocation and Mental Workload at Different Random Hazard Points on Prairie Highway

To identify the characteristics of driver’s visual perception and measure the mental workload at different random hazard points on prairie highway, an on-road study was conducted with 28 drivers. The I view X HED eye tracker and MP150 multichannel physiological recorder were used to collect the driver’s eye movement and ECG data at different hazard scenarios synchronously. The gaze transfer theory and statistical methods were used to make comparative analysis of typical visual and mental workload evaluation indicators of drivers at different random risk points. The results show that no matter what kind of random risk is confronted, the percentage of drivers’ fixation duration to the current lane drops, where random risk belongs to increase. The distribution of eye glance transition proportions shows that drivers highly bias their scanning attention by only focusing on transferring between forward and the areas where the random belongs to. Compared with off-road risk points, the driver’s gaze transfer is more frequent when facing on-road risk points, and the gaze transfer path is fixed, indicating that on-road risks have higher requirements for drivers’ perception and greater information processing load. There are obvious differences in the degree of influence of the types of random risk points on driver’s psychology. The heart rate growth rate is the largest when drivers were confronted with overtaking cut-in (37.9%) and forward parking (38%), whereas the index RMSSD changes in the opposite way. It reaches the minimum value when the random risks are overtaking cut-in (22.679 ms) and forward parking (22.907 ms). Meanwhile, the driving speed shows larger fluctuation at risk points on the road. This study reveals that on-road hazards pose greater threats to drivers, and it can contribute to a better understanding of the potential hazards on the prairie highways and provide suggestions for future application of advanced driver assistance systems which can warn drivers about potential hazards.

EMCI: A Novel EEG-Based Mental Workload Assessment Index of Mild Cognitive Impairment.

As aging deepens, early detection of mild cognitive impairment (MCI) is increasingly important to prevent Alzheimer Dementia (AD) and improve the quality of life of older adults. In recent years, a large number of studies focus on the abnormal brain cognitive function of MCI, while ignoring the quantitative evaluation of MCI's mental workload. In this study, we propose a workload index for MCI screening, named EMCI, which is a linear discriminant cumulative estimate of subjects' electroencephalography (EEG) power spectra in α and β rhythms. Then, we design a matched prototype system to verify the effectiveness of EMCI. The results show that the EMCI is sensitive to changes of subjects' mental workload, and is significantly lower in MCI than in HC (Health control), which may be precisely caused by cognitive dysfunction. The proposed EMCI index can be used for online assessment of mental workload in older adults, which can help achieve quick screening of MCI and provide a critical window for clinical treatment interventions.

The Impact of False Alarms on Mental Workload, Stress and Performance: Implications for Medical Settings

The excessive number of auditory and false alarms (FAs) in medical settings (e.g. ICUs) negatively impact alarm response and performance on ongoing tasks. We examined the impact of two FA rates (low-40% and high-80% in a counterbalanced order repeated measures design) on 36 participants’ alarm responses with a concurrent working-memory (N-back) task. Participants made significantly more responses to FAs in the high FA condition relative to the low FA condition. An FA rate and order interaction revealed that participants who started in the low FA condition responded to fewer FAs in both the high and low FA conditions. Participants who started in the high FA condition responded to more than twice the number of FAs in the high FA condition compared to the low FA condition. Similarly, response time to correct detections (Hits) in the alarm and N-back tasks also revealed interaction effects between order and FA conditions. Participants who received the low FA condition first responded faster to true alarms in the high FA condition compared to those who received the high FA condition first. No significant main effects or interactions were observed for the number of Hits on the alarm task, response times to FAs in either task, nor for subjective ratings of mental workload or stress. Significant main and interaction effects observed on the N-back task were likely attributable to practice effects. These results underscore the need to reduce the number of FAs in medical settings and have implications for designing training paradigms to improve provider performance.

Development of an Eye Response-Based Mental Workload Evaluation Method

This study proposed an eye responses-based mental workload (E-MWL) evaluation method in nuclear power plants (NPPs) when performing the task via a user interface control. The fuzzy theory was used to combine four eye response indices using the entropy weight method. Then, the E-MWL method was validated through experiments by comparison with the NASA-TLX rating and performance measures indices in two different tasks of the State Oriented Procedure (SOP) in NPP. The correlation analysis results between the NASA-TLX and eye response indices showed that four eye response indices used in this study were correlated significantly with the NASA-TLX, indicating that these indices may develop the E-MWL method. The E-MWL score results indicated that it is highly correlated with NASA-TLX and performance measures indices in two different tasks of SOP in NPP. This has proved that E-MWL is an objective method suitable for evaluating and predicting human mental workload (MWL) for interface control task in NPPs.