Levels Of Mental Workload Research Articles

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

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

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

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

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.

Analysis physical workload, mental workload, and work stress on teachers at Public Special School of Surakarta

Purpose: This study aimed to analyze the relationship between physical workload and mental workload towards work stress on special school teachers. In addition, it discovers the portrayal of the level of physical and mental workload and work stress.
 Methods: This study is a quantitative research through a cross-sectional approach. A total of 50 teachers was the sample. Variables in this study were physical workload, mental workload, and work stress. Data was collected with a pulse oximeter and questionnaires of NASA-TLX and PSS-10, and collected data was analyzed with the Spearman rank test.
 Results: The results of this study showed there is no relationship between physical workload and work stress. On the other hand, there is a relationship between mental workload and work stress (p-value=0.720; PR=0.762). Furthermore, 18% of teachers had a required improvement in physical workload, 12% had a very high mental workload, and 50% had moderate work stress (p-value=0.000; PR=0.552).
 Conclusions: This study indicated that there is no relationship between physical workload and work stress, in the other hand, there is a relationship between mental workload and work stress.

Mental Workload and Road Environment Complexity: Subjective Assessments

Since 2011, mental workload has been one of the top 10 causes of accidents in Malaysia, accounting for over a thousand fatalities yearly. Mental workload is defined as a comparable mediating variable to attention. The aim of this study is to identify the fundamental causes and influences that affect the difficulty of the road environment and vehicle task, as well as their impact on the driver’s health. In this study, subjective measurement is used to assess the level of mental workload of drivers which includes NASA-TLX and KSS on two different road environment condition namely urban and rural road environment. The findings show that the complexity of the road environment has a significant effect towards the mental workload of a driver. The average NASA-TLX score for urban roads is higher than the average score for rural roads while, the results for KSS level indicates higher drowsiness level among the drivers when driving on a rural road as opposed to an urban road. These results may serve as a guide for future research into the mental workloads of drivers in various types of complex road environments, as well as a practical guide for car manufacturers, agencies responsible for road safety, and researchers to improve the safety design of cars and consider external factors related to road environment complexity when designing roads.

Assessment of Seafarers’ Mental Workload: A Study on High Speed Craft

High speed craft has become prominent in maritime industry. Sophisticated electronic navigation equipment on bridge increases mental demands of high speed craft navigator. In the present study, mental workload of high speed craft navigators has been assessed in terms of bridge navigation operations. For this purpose, the pairwise comparison of the effects of Electronic Chart Display and Information System, Automatic Radar Plotting Aid, Automatic Identification System and autopilot on mental workload and Revised NASA Task Load Index questionnaires were carried out. High speed craft navigators sorted berthing, unberthing, navigation in coastal waters, navigation in restricted waters, and navigation on open sea operations according to their mental workload levels. In addition, they indicated the factors mostly affecting mental workload and the remarkable suggestions to keep the optimum level of mental workload. As result of these findings, considerations have been made for increasing the safety of high speed craft navigation.

Analisis Beban Kerja Mental Karyawan Departemen Engineering Pada PT. Z Menggunakan Metode NASA-TLX

Human resources (HR) work activities are very important for business processes, so human resource factors must be taken into account and paid attention to. This research was conducted at the Engineering Department of PT. Z is responsible for product design and product development. This research aims to measure the workload of Engineering Department employees at PT. Z in order to increase employee productivity. This research uses the National Aeronautics and Space Administration Workload Index (NASA-TLX) method. This method is used to analyze the mental workload felt by Engineering Department employees when carrying out work activities and is expected to improve an existing system to increase work comfort and productivity. Based on the final NASA TLX score for the Engineering Department of PT Z, five employees have a very high level of mental workload, two employees have a high level of mental workload, two employees have a medium level of mental workload, and one employee has a light level of mental workload. Then for the mental workload of PT employees. Z based on the NASA-TLX method obtained an average WWL value of 72.9, which means it is included in the mental load classification, therefore there is a need for work improvement. Keywords: Ergonomics, Mental Load, NASA-TLX, Workload

The Impact of Covid-19 Pandemic on Mental Workload and Fatigue of Students during Online and Offline Practicum

Learning activities underwent policy changes during the Covid-19 pandemic were carried out online and offline. During the Covid-19 pandemic, there were many ways that universities did to keep learning going, such as implementing online learning and online learning. Practical learning experiences changes in the teaching and learning process system due to university policies. This study aims to measure the level of mental workload and the level of student fatigue during online and offline practicums and to determine the relationship between mental workload and fatigue level. The methods used in this research are the National Aeronautics and Space Administration Task Load Index (NASA-TLX) and Subjective Self Rating Test (SSRT) methods. The results showed that the level of mental workload of students was in the high classification with the NASA-TLX dimensions of effort, mental needs, and performance. The level of student fatigue is in the moderate classification with part of the SSRT, namely the weakening of activities. The results of the correlation test show that there is a relationship between mental workload and student fatigue levels with a positive direction of relationship.

Analysis of Mental Workload and Fatigue Level in Online Learning during the Pandemic among Industrial Engineering Students (A Case Study at Buana Perjuangan University of Karawang)

Adjustments in online learning systems during the COVID-19 pandemic have led to an increase in mental workload and fatigue levels among students. This study aims to determine the influence of independent factors and the relationship between mental workload and fatigue levels among Industrial Engineering students during online learning. The research methods used were NASA-TLX and SOFI. The results show a significant influence of marital status on the level of mental workload with a p-value of 0.016, while there is no significant influence of student status and gender with p-values of 0.435 and 0.210. However, there is a significant influence of student status and gender on fatigue level with p-values of 0.008 and 0.000, while marital status has a significant influence with a p-value of 0.326. The correlation test results show that in the NASA-TLX method, 6 pairs of elements have a significant relationship with a p-value of 0.000, while the remaining 8 pairs of elements do not have a significant relationship with p > 0.05. Furthermore, the SOFI method shows all elements have a significant relationship with a p-value of 0.000. The findings of this study can serve as evaluation for institutions to create effective and successful online learning.

A novel non-intrusive mental workload evaluation concept in human-robot collaboration

The integration of Human-Robot Collaboration (HRC) in industrial robotics introduces challenges, particularly in adapting manufacturing environments to work seamlessly with collaborative robots. A key objective in HRC system optimization is enhancing human acceptance of these robots and improving productivity. Traditionally, the assessment of human mental workload in these settings relies on methods like EEG, fNIRS, and heart rate monitoring, which require direct physical contact and can be impractical in manufacturing environments. To address these issues, we propose an innovative and non-intrusive method that employs cameras to measure mental workload. This technique involves capturing video footage of human operators on the shop floor, focusing specifically on facial expressions. Advanced AI algorithms analyse these videos to predict heart rate ranges, which are then used to estimate mental workload levels in real time. This approach not only circumvents the need for direct contact with measurement devices but also enhances privacy and data security through privacy computing measures. Our proposed method was tested in an HRC experiment to provide preliminary validation. This pioneering use of non-intrusive AI-based vision techniques for real-time mental workload assessment represents a significant advancement in managing human factors in industrial HRC settings.

The Level of Psychological Tension and Professional Burnout in Nurses Employed in Hospital Emergency Departments

Burnout syndrome is a collection of adverse symptoms that affect nursing staff employed in medical facilities. This syndrome is marked by a low mood, mental fatigue, and physical discomfort. Its emergence is impacted by strenuous work, organizational problems, and interpersonal conflicts. The aim of this study was to analyze the degree of mental burden and occupational burnout of nurses employed in Hospital Emergency Departments (ER). The data collection was based on the author’s questionnaire, as well as the MBI Maslach occupational burnout questionnaire and the Meister’s mental burden assessment questionnaire. The sample comprised 113 professionally active nurses employed in the HED. Conclusions: (1) the respondents exhibit a low and medium level of occupational burnout and mental workload, (2) socio-demographic factors, work experience and prevention methods do not affect the level of mental workload and occupational burnout, (3) employment in several workplaces causes a higher level of workload and professional burnout, (4) satisfaction with remuneration and support from colleagues is a key factor in reducing the risks of professional burnout.

Correlation of Work Fatigue and Mental Workload in Train Drivers: A Cross-sectional Study.

Evidence suggests that train drivers experience a high level of fatigue and mental workload. The present study aimed to assess overall, physical, and mental fatigue levels and their correlations with the mental workload in the metro train operation. Study Design: A cross-sectional study. This study was conducted on all 1194 train drivers in the Tehran Metro. The train drivers completed the Samn-Perelli Fatigue Scale and the Fatigue Assessment Scales at the beginning and end of the shift. In addition, they completed the National Aeronautics and Space Administration Task Load Index in the middle and at the end of the shift. Correlation and regression analyses were performed on the data to test the study hypothesis. Overall, physical, and mental fatigue levels increased significantly at the end of the shift compared to the onset of the shift (P<0.001). The mental workload and related dimensions were significantly increased at the end of the shift compared to the middle of the shift (P<0.001). Mental demand was the most important workload problem among the train drivers. The highest correlation was found between overall workload and time pressure (R=0.68, P<0.001). The mental workload had a significant correlation with work fatigue in the train drivers. Control measures should be focused on the mental workload and related dimensions, especially mental demand and time pressure.

The relationship between mental workload and nurse stress levels in hospitals

High mental workload implies significant mental resources and can lead to increased cognitive stress and fatigue. Nurses have the responsibility of managing a wide range of nursing care tasks, which frequently involve addressing anxiety, handling patient complaints, and managing patient defense mechanisms, all of which can lead to increased stress levels. This study aimed to identify the relationship between mental workload and the stress level of nurses in the Hospital Malang City. The research utilized a cross-sectional study design. The sampling technique employed was Total Sampling, involving 96 inpatient nurses at the hospital. Data collection involved the use of two questionnaires: the National Aeronautics and Space Administration Task Load Index (NASA-TLX) and the Perceived Stress Scale (PSS-10). Data analysis in this study was conducted using Pearson Correlation. The results indicated a positive relationship between mental workload and nurses' stress levels, supported by a significant p-value of 0.002 and a correlation coefficient of 0.312. A high mental workload can impact the level of job-related stress experienced by nurses. Effective stress management skills can help individuals mitigate this impact.

Analisa Beban Mental Karyawan Menggunakan Nasa - Tlx Rooms Division Prime Plaza Hotel Kualanamu

This research aims to analyze the mental workload of Prime Plaza Hotel Kualanamuemployees and provide recommendations to management to overcome the decline in service qualitydue to workload. Data was taken from the Rooms Division which consists of the front officedepartment and housekeeping department with a total of 18 respondents from various levels ofpositions. The NASA-TLX method was used to process the data, and the results showed that theworkload in the Rooms Division was very high based on six indicators. This research aims tounderstand the level of mental workload on employees after the Covid-19 pandemic and provideinput to management for more effective workload distribution. The research results show the needfor additional training for supervisors and additional workforce in the Front Office area, such asreservations, drivers and security. Training is also needed for supervisors and staff in thehousekeeping area so that work becomes more efficient.

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

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

Identifying stable EEG patterns over time for mental workload recognition using transfer DS-CNN framework

Timely evaluating operator Mental Workload (MW) levels in human–computer interaction helps to assign tasks legitimately and guarantee safety and efficiency at the same time. This study presented a stable Electroencephalogram (EEG) pattern over time. A data augmentation (DA) method and a deep neural network (NN) are developed accordingly based on the peculiarity of the EEG pattern for MW assessment. The EEG pattern consists of rhythmic energies in the Temporal–Spatial–Spectral domain, named energy tensor. The DA method for energy tensors is realized by randomly disrupting the temporal dimension which was certified to be irrelevant to MW levels in our previous study. The NN is designed based on deep transfer learning and Depthwise Separable Convolutional Neural Networks (DS-CNN). Energy tensors after DA are used as the input of the proposed NN to construct MW classifiers and the entire operation is named transfer DS-CNN framework. The experiment results demonstrate that the optimal temporal resolution of EEG patterns for MW classification is 3.5 min. The transfer DS-CNN framework is effective enough to build a cross-session MW classifier for a specific subject in an average of 9.195 s, with an average prediction accuracy of 97.22%. Our study suggests that the stable EEG patterns and the DA method preserve features for cross-session MW recognition. It is befitting to discriminate MW levels in real-time with the transfer DS-CNN framework.

Neck Muscle Coactivation Response to Varied Levels of Mental Workload During Simulated Flight Tasks.

To evaluate neck muscle coactivation across different levels of mental workload during simulated flight tasks. Neck pain (NP) is highly prevalent among military aviators. Given the complex nature within the flight environment, mental workload may be a risk factor for NP. This may induce higher levels of neck muscle coactivity, which over time may accelerate fatigue, increase neck discomfort, and affect flight task performance. Three counterbalanced mental workload conditions represented by simulated flight tasks modulated by interstimulus frequency and complexity were investigated using the Modifiable Multitasking Environment (ModME). The primary measure was a neck coactivation index to describe the neuromuscular effort of the neck muscles as a system. Additional measures included perceived workload (NASA TLX), subjective discomfort, and task performance. Participants (n = 60; 30M, 30F) performed three test conditions over 1hr each while seated in a simulated seating environment. Neck coactivation indices (CoA) and subjective neck discomfort corresponded with increasing level of mental workload. Average CoAs for low, medium, and high workloads were: .0278(SD = .0232), .0286(SD = .0231), and .0295(SD = .0228), respectively. NASA TLX mental, temporal, effort, and overall scores also increased with the level of mental workload assigned. For ModME task performance, the overall performance score, monitoring accuracy, and resource management accuracy decreased while reaction times increased with the increasing level of mental workload. Communication accuracy was lowest with the low mental workload but had higher reaction times relative to increasing workload. Mental workload affects neck muscle coactivation during combinations of simulated flight tasks within a simulated helicopter seating environment. The results of this study provide insights into the physical response to mental workload. With increasing multisensory modalities within the work environment, these insights may assist the consideration of physical effects from cognitive factors.

Nurses' mental workload and public health emergency response capacity in COVID-19 pandemic: A cross-sectional study.

The aim of this study was to assess the level of mental workload of Chinese nurses through a latent profile analysis and to explore its relationship with public health emergency response capacity. A cross-sectional design with a convenience sample. A convenience sample of nurses from five tertiary hospitals in Chengdu between Mayand December 2022. Demographic, work-related information, Nurse's version of NASA's Task Load Index Scale and Nurse's Public Health Emergency Response Capacity Scale were used in this study. The mean scores for mental workload and emergency response capacity for nurses were (57.19 ± 15.67) and (3.58 ± 0.77) respectively. We found that the mental workload of nurses fell into three potential categories. In addition, there were differences in psychological training and supply of epidemic prevention materials in the department among nurses with different mental workload subtypes. There was a moderate negative correlation between nurses' mental workload and public health emergency response capacity. Our results show that there is still a strong mental workload on a proportion of nurses, and enhanced psychological training and material supply support are beneficial in relieving nurses' mental workload. The better the nurses' capacity to cope with public health emergencies, the lower their mental workload. Nursing managers should pay ongoing attention to the mental workload status of nurses in the latter stages of a pandemic and individual differences in nurses' mental workload. In addition, nursing managers should be aware of the impact of public health emergency response capacity on nurses' mental workload. They can intervene in nurses mental workload from a new perspective. 560 registered nurses participated in this study.

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

Objective This study aimed to examine the impact of non-driving-related tasks (NDRTs) on drivers in highly automated driving scenarios and sought to develop a deep learning model for classifying mental workload using electroencephalography (EEG) signals. Methods The experiment involved recruiting 28 participants who engaged in simulations within a driving simulator while exposed to 4 distinct NDRTs: (1) reading, (2) listening to radio news, (3) watching videos, and (4) texting. EEG data collected during NDRTs were categorized into 3 levels of mental workload, high, medium, and low, based on the NASA Task Load Index (NASA-TLX) scores. Two deep learning methods, namely, long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM), were employed to develop the classification model. Results A series of correlation analyses revealed that the channels and frequency bands are linearly correlated with mental workload. The comparative analysis of classification results demonstrates that EEG data featuring significantly correlated frequency bands exhibit superior classification accuracy compared to the raw EEG data. Conclusions This research offers a reference for assessing mental workload resulting from NDRTs in the context of highly automated driving. Additionally, it delves into the development of deep learning classifiers for EEG signals with heightened accuracy.

Impact of preferred surgical modality on surgeon wellness: a survey of workload, physical pain/discomfort, and neuromusculoskeletal disorders

BackgroundWe compared surgeons’ workload, physical discomfort, and neuromusculoskeletal disorders (NMSDs) across four surgical modalities: endoscopic, laparoscopic, open, and robot-assisted (da Vinci Surgical Systems).MethodsAn electronic survey was sent to the surgeons across an academic hospital system. The survey consisted of 47 questions including: (I) Demographics and anthropometrics; (II) The percentage of the procedural time that the surgeon spent on performing each surgical modality; (III) Physical and mental demand and physical discomfort; (IV) Neuromusculoskeletal symptoms including body part pain and NMSDs.ResultsSeventy-nine out of 245 surgeons completed the survey (32.2%) and 65 surgeons (82.2%) had a dominant surgical modality: 10 endoscopic, 15 laparoscopic, 26 open, and 14 robotic surgeons. Physical demand was the highest for open surgery and the lowest for endoscopic and robotic surgeries, (all p < 0.05). Open and robotic surgeries required the highest levels of mental workload followed by laparoscopic and endoscopic surgeries, respectively (all p < 0.05 except for the difference between robotic and laparoscopic that was not significant). Body part discomfort or pain (immediately after surgery) were lower in the shoulder for robotic surgeons compared to laparoscopic and open surgeons and in left fingers for robotic surgeons compared to endoscopic surgeons (all p < 0.05). The prevalence of NMSD was significantly lower in robotic surgeons (7%) compared to the other surgical modalities (between 60 and 67%) (all p < 0.05).ConclusionsThe distribution of NMSDs, workload, and physical discomfort varied significantly based on preferred surgical approach. Although robotic surgeons had fewer overall complaints, improvement in ergonomics of surgery are still warranted.Graphical abstract