Evaluation Of Mental Workload Research Articles

Mental Effort and Information-Processing Costs Are Inversely Related to Global Brain Free Energy During Visual Categorization.

Mental effort is a neurocognitive process that reflects the controlled expenditure of psychological information-processing resources during perception, cognition, and action. There is a practical need to operationalize and measure mental effort in order to minimize detrimental effects of mental fatigue on real-world human performance. Previous research has identified several neurocognitive indices of mental effort, but these indices are indirect measures that are also sensitive to experimental demands or general factors such as sympathetic arousal. The present study investigated a potential direct neurocognitive index of mental effort based in theories where bounded rational decision makers (realized as embodied brains) are modeled as generalized thermodynamic systems. This index is called free energy, an information-theoretic system property of the brain that reflects the difference between the brain’s current and predicted states. Theory predicts that task-related differences in a decision makers’ free energy are inversely related to information-processing costs related to task decisions. The present study tested this prediction by quantifying global brain free energy from electroencephalographic (EEG) measures of human brain function. EEG signals were recorded while participants engaged in two visual categorization tasks in which categorization decisions resulted from the allocation of different levels of mental information processing resources. A novel method was developed to quantify brain free energy from machine learning classification of EEG trials. Participant information-processing resource costs were estimated via computational analysis of behavior, whereas the subjective expression of mental effort was estimated via participant ratings of mental workload. Following theoretical predictions, task-related differences in brain free energy negatively correlated with increased allocation of information-processing resource costs. These brain free energy differences were smaller for the visual categorization task that required a greater versus lesser allocation of information-processing resources. Ratings of mental workload were positively correlated with information-processing resource costs, and negatively correlated with global brain free energy differences, only for the categorization task requiring the larger amount of information-processing resource costs. These findings support theoretical thermodynamic approaches to decision making and provide the first empirical evidence of a relationship between mental effort, brain free energy, and neurocognitive information-processing.

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new traffic safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers’ mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we finished nonparametric test of the drivers’ psychological load and road test. The results show that in compassion with the daytime scenario, drivers should pay much effort to driving skills and risk identification in the nighttime scenario. Thus, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions.

Mental workload evaluation and its application in train driving multitasking scheduling: a Timed Petri Net-based model

Mental workload (MW) plays an important role in the task design of safety–critical systems, which varies across different operators in a given task and affects the performance. It is still a problem to provide a quantitative definition of MW and apply it to the system task optimization design more effectively. Based on this idea, this paper presents a Timed Petri Net (TPN)-based MW evaluation model. The departure phase multitasking scheduling of train driving was treated as an application to concretely represent how does the model improve task design and performance based on the MW evaluation results of individuals. A validation experiment was deployed in a driving simulator, and the Euclidean distance between MW and reaction time was taken as the index of task optimization. Results indicate that the index of the optimized task has a lower value than the original, which means participants achieve a better balance between MW and reaction time in the optimized task design. Besides the design of driver advisory systems, the method is expected to be useful for improving the evaluation accuracy and application efficiency of MW.

EEG-Based Mental Workload Estimation.

Knowledge of the level of mental workload induced by any task is essential for optimizing load share among the operators. This helps in assessing their capability; besides, helping in task allocation. Since a persistently high workload experienced by operators such as aircraft pilots and automobile drivers many times compromises their performance and safety. Despite the availability of various mental workload evaluation techniques such as heart rate variability, pupil dilation, sac-cades, etc., assessment of mental workload is still a challenging task. In this work, we aim to evaluate the workload of the operator involved in long duration tasks. For this, experiments have been carried out in a working environment which provides tasks to be done simultaneously, tasks with a pause or break in activity and cross-functional tasks. The experiment data is recorded continuously in different modes and analyzed in segments to show the change in mental workload. The artificial neural network (ANN) architecture classified the workload data with an accuracy of 96.6%. The brain connectivity analysis shows the efficacy of the proposed approach.

Modeling pilot mental workload using information theory

AbstractPredicting mental workload of pilots can provide cockpit designers with useful information to reduce the possibility of pilot error and cost of training, improve the safety and performance of systems, and increase operator satisfaction. We present a theoretical model of mental workload, using information theory, based on review investigations of how effectively task complexity, visual performance, and pilot experience predict mental workload. The validity of the model was confirmed based on data collected from pilot taxiing experiments. Experiments were performed on taxiing tasks in four different scenarios. Results showed that predicted values from the proposed mental workload model were highly correlated to actual mental workload ratings from the experiments. The findings indicate that the proposed mental workload model appears to be effective in the prediction of pilots’ mental workload over time.

Investigating the Effects of Mental Workload on Highway Safety

The majority of vehicle collisions occur because of human error; in fact, studies have shown that approximately 95% of collisions are caused, at least in part, as a result of human mistakes. Therefore, it is important to study the main causes of human mistakes and develop mitigation strategies to reduce, if not eliminate, these errors from occurring. In this respect, designing highways that balance mental workload is a crucial task that ensures drivers have sufficient time to make appropriate decisions. However, the quantitative relationship between mental workload and collisions is not well documented in the safety literature. Enough evidence exists to support the reasonable conclusion that safety is affected by changes in workload, but there is no quantitative evidence of this effect. Consequently, this paper tries to investigate the relation between mental workload ratings and collisions on highways using data from Alberta, Canada. Horizontal and vertical curve parameters on two-lane, two-way highways were first extracted from LiDAR and GPS data using MATLAB algorithms, and the resulting features were summarized using Civil 3D. The mental workload ratings were assigned based on the presence of four major geometric features, namely, intersections, horizontal curves, vertical curves, and changes in cross-section. The results show that mental workload has a significant effect on safety for two-way, two-lane highways. Furthermore, the findings strongly indicated the need to integrate mental workload into the design process, to not only meet the operational needs of the highway, but also ensure that the geometric layout does not mentally overwhelm drivers.

Functional near-infrared spectroscopy in the evaluation of urban rail transit drivers’ mental workload under simulated driving conditions

The objective of this study is to investigate the potential of functional near-infrared spectroscopy (fNIRS) combined with heart rate variability indices, for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions, particularly during task engagement and disengagement. Experienced metro drivers wearing fNIRS monitoring systems were asked to drive for 90 min in a professional metro driving simulator. Workload stimulus tasks were added and an n-back task (n = 3) was implemented to induce workload in the simulated driving experiment. Experimental results indicate that fNIRS are sensitive to mental workload and reliable for discriminating the degree of mental workload. Research findings demonstrate the feasibility and reliability of fNIRS as a tool for real-time evaluating and monitoring driver mental workload along with task factors from a perspective of brain activations during simulated or actual driving.Practitioner Summary: This study provides evidence for the potential of functional near-infrared spectroscopy (fNIRS) for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions. The first fNIRS application to mental workload evaluation in the field of urban rail transportation helps companies develop reasonable shiftwork schedule and ensure operation safety.Abbreviations: fNIRS: functional near-infrared spectroscopy; oxy-Hb: Oxy-hemoglobin; NASA-TLX: National Aeronautics and Space Administration Task Load Index; EEG: electroencephalogram; ECG: electrocardiogram; HRV: variability; LF: low-frequency power; HF: high-frequency power; PFC: prefrontal cortex; NIRS: near-infrared spectroscopy; DWT: discrete wavelet transform; EMG: electromyography; DT: determination test; TP: total power; LFnorm: standardized LF; HFnorm: standardized HF; VLF: very low frequency; deoxy-Hb: deoxy-hemoglobin.

Learning rate and subjective mental workload in five truck driving tasks

Both learning curve models and subjective mental workload are useful tools for determining the length of training for new workers and predicting future task performance. An experiment was designed to collect the task completion times and subjective mental workload of five driving tasks including (a) reverse into garage, (b) 3-point turn, (c) parallel parking, (d) S-curve and (e) up-down-hill. The results indicated that task completion times of truck driving can be predicted with a learning curve. Practice significantly reduced the mental workload rating. However, the novice trainees tended to have a more significant reduction because, compared to experienced trainees, they tended to give greater or lower workload scores than the experienced trainees before and after practice, respectively. The current study may not be complete enough to provide guidelines for a training programme, but it is adequate to suggest that learning rate and workload measure can serve as indexes for factoring in the individual differences.Practitioner summary: Learning curves can be used to determine the length of training for new workers and performance standards for a particular task. Learning rate and mental workload were found to be important measures for comparing individual differences in order to better design a training programme. However, mental workload must be evaluated by experienced participants.

The peak-end effects in controllers’ mental workload evaluation

People’ retrospective evaluation of their hedonic experience is heavily influenced by the most intense (peak) and the last (end) moment. This study examines whether such peak-end effect can also be applied to the evaluation of mental workload in the context of air traffic control. Twenty professional approach controllers finished four tasks on high fidelity simulators and rated overall workload after each task by NASA-TLX scale. A 2 (high vs. low peak) by 2 (high vs. low end) within-subject design was used. The results showed that there was a significant peak effect and a marginally significant end effect. This study calls for further investigation of the measurement issue of mental workload and provides insights for workload management strategies.

Mental workload is reflected in driver behaviour, physiology, eye movements and prefrontal cortex activation

Mental workload is an important factor during driving, as both high and low levels may result in driver error. This research examined the mental workload of drivers caused by changes in road environment and how such changes impact upon behaviour, physiological responses, eye movements and brain activity. The experiment used functional near infrared spectroscopy to record prefrontal cortex activation associated with changes in mental workload during simulated driving. Increases in subjective ratings of mental workload caused by changes in road type were accompanied by increases in skin conductance, acceleration signatures and horizontal spread of search. Such changes were also associated with increases in the concentration of oxygenated haemoglobin in the prefrontal cortex. Mental workload fluctuates during driving. Such changes can be identified using a range of measures which could be used to inform the development of in-vehicle devices and partially autonomous systems.

Estimating autonomic nerve activity using variance of thermal face images

In this paper, we propose a novel method for evaluating mental workload (MWL) using variances in facial temperature. Moreover, our method aims to evaluate autonomic nerve activity using single facial thermal imaging. The autonomic nervous system is active under MWL. In previous studies, temperature differences between the nasal and forehead portions of the face were used in MWL evaluation and estimation. Hence, nasal skin temperature (NST) is said to be a reliable indicator of autonomic nerve activity. In addition, autonomic nerve activity has little effect on forehead temperature; thus, temperature differences between the nasal and forehead portions of the face have also been demonstrated to be a good indicator of autonomic nerve activity (along with other physiological indicators such as EEG and heart rate). However, these approaches have not considered temperature changes in other parts of the face. Thus, we propose novel method using variances in temperature for the entire face. Our proposed method enables capture of other parts of the face for temperature monitoring, thereby increasing evaluation and estimation accuracy at higher sensitivity levels than conventional methods. Finally, we also examined whether further high-precision evaluation and estimation was feasible. Our results proved that our proposed method is a highly accurate evaluation method compared with results obtained in previous studies using NST.

Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces-an Offline Study.

Brain-computer interfaces (BCIs), independent of the brain's normal output pathways, are attracting an increasing amount of attention as devices that extract neural information. As a typical type of BCI system, the steady-state visual evoked potential (SSVEP)-based BCIs possess a high signal-to-noise ratio and information transfer rate. However, the current high speed SSVEP-BCIs were implemented with subjects concentrating on stimuli, and intentionally avoided additional tasks as distractors. This paper aimed to investigate how a distracting simultaneous task, a verbal n-back task with different mental workload, would affect the performance of SSVEP-BCI. The results from fifteen subjects revealed that the recognition accuracy of SSVEP-BCI was significantly impaired by the distracting task, especially under a high mental workload. The average classification accuracy across all subjects dropped by 8.67% at most from 1- to 4-back, and there was a significant negative correlation (maximum r = −0.48, p < 0.001) between accuracy and subjective mental workload evaluation of the distracting task. This study suggests a potential hindrance for the SSVEP-BCI daily use, and then improvements should be investigated in the future studies.

Correlative Evaluation of Mental and Physical Workload of Laparoscopic Surgeons Based on Surface Electromyography and Eye-tracking Signals

Surgeons’ mental and physical workloads are major focuses of operating room (OR) ergonomics, and studies on this topic have generally focused on either mental workload or physical workload, ignoring the interaction between them. Previous studies have shown that physically demanding work may affect mental performance and may be accompanied by impaired mental processing and decreased performance. In this study, 14 participants were recruited to perform laparoscopic cholecystectomy (LC) procedures in a virtual simulator. Surface electromyography (sEMG) signals of the bilateral trapezius, bicipital, brachioradialis and flexor carpi ulnaris (FCU) muscles and eye-tracking signals were acquired during the experiment. The results showed that the least square means of muscle activity during the LC phases of surgery in an all-participants mixed effects model were 0.79, 0.81, and 0.98, respectively. The observed muscle activities in the different phases exhibited some similarity, while marked differences were found between the forearm bilateral muscles. Regarding mental workload, significant differences were observed in pupil dilation between the three phases of laparoscopic surgery. The mental and physical workloads of laparoscopic surgeons do not appear to be generally correlated, although a few significant negative correlations were found. This result further indicates that mental fatigue does markedly interfere with surgeons’ operating movements.

Drivers’ Interaction with Adaptive Cruise Control on Dry and Snowy Roads with Various Tire-Road Grip Potentials

This study investigates drivers’ interaction with Adaptive Cruise Control (ACC) in different road conditions and identifies areas of improvement. Ninety-six drivers drove with the ACC in a driving simulator showing either a summer scenery and a dry road with high grip potential or a winter scenery with a snowy road and reduced grip potential. The results show that on snowy roads the drivers set in average a lower ACC speed and preferred a larger ACC time gap. Drivers’ workload and effort were higher when using the ACC on snowy as compared to dry roads. Generally, the use of a shorter ACC gap resulted in lower ratings of comfort, safety, and trust and higher ratings of mental workload and effort in both dry and snowy road conditions. The drivers judged that ACC was braking too late and maintained a too short gap to the forward vehicle, especially when the ACC was set to 1 second as compared to a 1.8-second time gap. A future adaptation of ACC’s control strategy to reduced tire-road grip potential would not only improve comfort and user acceptance of the human driver but also increase the potential to react in emergency situations with braking or evasive steering.

Cross-session classification of mental workload levels using EEG and an adaptive deep learning model

Abstract Evaluation of operator Mental Workload (MW) levels via ongoing electroencephalogram (EEG) is quite promising in Human-Machine (HM) collaborative task environment to alarm the temporal operator performance degradation. However, accurate recognition of MW states via a static pattern classifier with training and testing EEG signals recoded on separate days is particularly challenging as EEG features are differently distributed across different sessions. Motivated by the superiority of the deep learning approaches for stable feature abstractions in higher levels, an adaptive Stacked Denoising AutoEncoder (SDAE) is developed to tackling such cross-session MW classification task in which the weights of the shallow hidden neurons could be adaptively updated during the testing procedure. The generalization capability of the adaptive SDAE is first evaluated under within/cross-session conditions. Then, we compare it with the state of the art MW classifiers under different feature selection and the noise corruption paradigms. The results indicate a higher performance of the adaptive SDAE in dealing with the cross-session EEG features. By analyzing the optimal step length, the data augmentation scheme and the computational cost for iterative tuning, the adaptive SDAE is also demonstrated to be acceptable for online implementation.

Open plan offices as sociotechnical systems: What matters and to whom?

Open plan offices have proliferated for the past several decades with more and more workers being concentrated in office buildings. Several studies have identified a number of negative factors associated with open plan offices, and those include noise, speech interference, lack of privacy, and a perceived loss of control over work. While negative factors have been identified several times in the literature, many studies rely on either surveys or highly controlled environments. The objective of this study was to use a quasi-naturalistic environment based on a scaled world model to test three hypotheses of the impacts of office interference on editing and web navigation performance and mental workload. A mixed factor design was used that utilized continuous speech recordings, discontinuous speech recordings, and a quiet condition. Individual differences questionnaires were administered to measure focused attention, stress, and cognitive failures. After task completion, participants recorded mental workload ratings. Participants had higher performance accuracy in the document editing task across the speech interference conditions. Mental workload ratings were higher in the web navigation task in the discontinuous speech condition compared to the continuous speech condition. In contrast to the hypothesis, those reporting stronger focused attention performed more poorly across all speech conditions. Overall, the results were mixed. OPO work design must focus on individual differences among workers to determine how to customize design to facilitate performance.

Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners

Electroencephalogram is the electrical phenomena in the cerebral cortex or the scalp surface due to the electrophysiological activity of brain cells. Electroencephalogram has great theoretical and practical significance in measuring mental workload of people. More precise electroencephalographic is a precondition to study mental workload of miners. In this article, based on the actual situation of the electroencephalographic measurement of miners, the particle swarm optimization is introduced to improve the standard genetic algorithm, and put forward a combined method integrating the genetic algorithm with particle swarm optimization for achieving electroencephalogram-based measures of miners' mental workload. Moreover, the MATLAB simulation platform is used for simulation testing. Testing results prove the effectiveness of the combined method.

Advanced algorithms for cloud and modern information systems

Existing information systems are experiencing a bottleneck in dealing with complexity, dynamics, and uncertainties in their applications. After the technologies of integrated circuits, personal computers, and the Internet, Cloud computing is the latest information technology that is radically changing our daily life. With the fast development of cloud computing technology, various modern information network systems such as navigation system, fault Diagnosis system, indoor position system, resource management system, face detection system, and large-scale information system have achieved great advancement in recent years. The cloud and modern information systems should support data distribution, resource management, incomplete information processing, etc. This special issue brings together research articles covering advanced algorithms for cloud and modern information systems, including intelligent collision avoidance model for submarine in underwater navigation environment, fault diagnosis system for large diesel fuel engine, RFID-based three-dimensional (3D) indoor positioning system (IPS), QoS-aware mechanism in cloud, low-power mapping algorithm for 3D network on chip (NoC), new cascade classifier system for face detection, and improved genetic algorithm in the electroencephalographic (EEG)-based mental workload evaluation for miners. The papers in this special issue are arranged as follows. Yan-hua Liang and Chengtao Chen in the first paper ‘‘Intelligent Collision Avoidance based on 2D Risk model’’ proposed a novel and efficient collision risk model for handling with the challenging and imperative issue. The concepts of detection domain and fuzzy logic are adopted for modelling the degree of collision risk. The detection domain is used as the submarine safety scope when navigating under water, while the fuzzy logic is used as the mathematical implement for the analysis and synthesis of relations between obstacles or other submarines that are met in the navigation environment. The collision risk is regarded as one effective evaluation function that reflects which exact action should be done when the threshold of the risk degree is met. The second paper by Li et al. ‘‘Fault Diagnosis for Large Diesel Fuel Engine Based on Chaotic Fractal Method’’ investigates the chaotic fractal characteristics during the combustion process of the diesel engine fuel system to build an effective fault diagnosis method for large fuel engine based on chaotic fractal method. Considering the two kinds of typical failure modes, the two important characteristic parameters variation trend for chaotic system, namely, correlation dimensions, and the calculation methods of the maximum Lyapunov exponent are discussed. The results showed that the maximum Lyapunov exponent method is more proper to system fault detection. In the third paper ‘‘A Novel RFID ThreeDimensional Indoor Positioning System based on Trilateral Positioning Algorithm,’’ Xu et al. design and implement the RFID IPS based on received signal strength indicator (RSSI), which obtains 3D coordinates orientation through multiple trilateral positioning algorithm on location tag. RFID is the key component of the internet of things (IoT) and many fusion application systems combining RFID and cloud computing are emerging. The authors’ work in the IPS can deal with the problem that GPS system cannot provide indoor positioning services. Ye et al. in the fourth paper ‘‘A Novel QoS-Aware Mechanism for Provisioning of Virtual Machine Resource in Cloud’’ focus on efficient low-level resource provisioning and QoS guaranteed. A novel adaptive QoS-aware virtual machine provisioning mechanism is proposed to ensure efficient utilization of the system low-level resources. The VM for similar type of requests has been recycled so that the VM creation time can be minimized and used to serve more user requests. In the proposed model, QoS is linked by

A robotics operator manager role for military application

Due to the increasing use of robots in US military operations, a new role may help to manage and coordinate multiple robotic assets, especially when considering the high mental workload requirements for robot control. The role of a robotics operator manager (ROM) was formed to study a path forward in solving this problem. An experiment with human test participants acting as ROMs showed that the ROM role as constructed is a workable solution to the robot management problems and also showed that time limits and bandwidth limitation affected engineered explosive detection performance, assessments of trust, and ratings of mental workload. Some of these results and others showing preferences for image stimuli and ground robots were simulated in an Adaptive Control of Thought–Rational model of the ROM experiment. The experimental and model results reveal that the ROM is not just a workable solution to the robot coordination problem, but that it may also be a preferred solution. I conclude with notes on how to field the role with personnel of specific skill sets.

Evaluation of Mental Workload among ICU Ward's Nurses.

Background: High level of workload has been identified among stressors of nurses in intensive care units (ICUs). The present study investigated nursing workload and identified its influencing perfor­mance obstacles in ICUs.Methods: This cross-sectional study was conducted, in 2013, on 81 nurses working in ICUs in Imam Khomeini Hospital in Tehran, Iran. NASA-TLX was applied for assessment of workload. Moreover, ICUs Performance Obstacles Questionnaire was used to identify performance obstacles associated with ICU nursing. Results: Physical demand (mean=84.17) was perceived as the most important dimensions of workload by nurses. The most critical performance obstacles affecting workload included: difficulty in finding a place to sit down, hectic workplace, disorganized workplace, poor-conditioned equipment, waiting for using a piece of equipment, spending much time seeking for supplies in the central stock, poor quality of medical materials, delay in getting medications, unpredicted problems, disorganized central stock, outpatient surgery, spending much time dealing with family needs, late, inadequate, and useless help from nurse assistants, and ineffective morning rounds (P-value<0.05).Conclusion: Various performance obstacles are correlated with nurses' workload, affirms the significance of nursing work system characteristics. Interventions are recommended based on the results of this study in the work settings of nurses in ICUs.