Indicator Of Mental Workload Research Articles

Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving

The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver’s mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver’s mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests.

Pattern analysis of physiological data for the assessment of mental workload

Measuring mental workload at the workplace using (psycho-) physiological measurement techniques seems desirable but is difficult to implement. Conventional analysis techniques are designed to cover longer measurement durations, neglecting the demands of modern work places: high worker flexibility and constantly fluctuating mental workload. As an alternative analysis approach, measurement (resp. analysis) duration can be shortened and event-based pattern analysis of various physiological parameters can be performed. The effects of such approaches are demonstrated by experimental examples. Furthermore, an event-timestamp independent framework is presented. Focusing on occasionally occurring peaks and longer lasting plateaus in mental workload trajectories, an automatized analysis of workload during work processes becomes possible.Practical relevance: With steadily increasing cognitive demands at work the risk of mental fatigue increases too. Mental workload is not directly observable at the workplace and the objective measurement and interpretation is complicated. Improving the overall assessment and analysis strategies for (physiological) mental workload indicators can benefit the quality of risk assessments of workplaces and processes as well as enable the possibility of demand-orientated control of (informational) assistance systems to prevent mental overload and resulting health constraints.

Evaluating Changes in Mental Workload in Indoor and Outdoor Ultra-Distance Cycling.

Whilst increasing mental workload has been shown to have a detrimental effect on cycling performance and more generally to increase the risk of harm, no studies have measured how mental workload changes as a function of ultra-distance cycling, indoors or outdoors. Our objective was to measure the difference in mental workload, as indicated by changes in EEG theta power, components of HRV and psychomotor vigilance and as reported using the ‘NASA Task Load Index questionnaire’, before and after a 5 h indoor ride and outdoor ride completed at 65% of functional threshold power. Results of the NASA-TLX indicated the mental demand of outdoor cycling to be significantly less than that of indoor cycling. There were significant differences in the PVT results between the pre and the post outdoor ride average and median response times. The slowest 10% PVT responses were significantly slower pre than post the indoor ride. There were significant differences in HRV between pre and post outdoor and indoor rides, specifically, in the average RR intervals, RMSSD (ms2), LFPower (ms2), NN50. There were modest changes in indicators of mental workload during an ultra-distance cycle ride. As such, mental workload during ultra-distance cycling is unlikely to be a contributory factor to decreases in performance or to an increased likelihood of accident and injury.

Scanpath analysis into the wild: the spatiotemporal distribution of fixations as an indicator of driver’s mental workload

Past studies using the distribution of eye fixations as an indicator of mental workload are limited to simulations and laboratory tasks. Hence, this assessment strategy has not yet been proven useful in real- world settings. In order to bridge this gap, in this study eye movements of a group of individuals were recorded while driving a car in a suburban road. Drivers’ scanpaths during driving and during driving while performing mundane secondary tasks were compared in this study. A more grouped pattern of fixations was expected in the dual-task condition than in the driving-only condition. As expected, results showed the effectiveness the spatiotemporal distribution of fixations in correctly discriminating between task load conditions, therefore indicating its usefulness for assessing mental workload also in complex real-world tasks.

Sensor-based indicators of performance changes between sessions during robotic surgery training

Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees’ cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (rrm=−.25,p<.001) and gaze entropy (rrm=−.37,p<.001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.

Is either peripheral detail(s) or central feature(s) easy to mentally process?: EEG examination of mental workload based on construal level theory

The same phenomenon or action may evoke different imaginations across the minds of people. For instance, locking the door could be construed either as putting the key into the lock or as securing the house. According to the construal level theory, the former (latter) is more likely to happen among people with a low (high) level of construal, who have an intrinsic inclination to focus on peripheral details (central features) of the phenomena or action. Given that object’s peripheral details are more concrete and less abstract than its central features, people with a low (high) level of construal have better developed concrete-mindset (abstract-mindset). Previous research has been consistently assuming that people with a low (high) level of construal are better at cognitively processing the concrete (abstract) phenomenon or action. However, this assumption has not yet been empirically tested with EEG methodology. In this paper, we test this assumption through within-subject experimental design (n = 44) conducting EEG methodology. Results of the P-300 component of the event-related brain potential, which is an indicator of mental workload, demonstrated that people with a low (high) level of construal experienced a greater mental workload when they were processing abstract (concrete) phenomena.

Task-irrelevant Auditory Event-related Potentials as Mental Workload Indicators: A Between-task Comparison Study.

Real-time monitoring of mental workload (MWL) is a crucial step to build closed-loop adaptive aiding systems for human-machine systems. MWL estimators based on spontaneous electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to achieve this goal. However, the previous studies show that the between-task robustness of these EEG/ERP-based MWL estimators is still an unsolved intractable question. This study attempts to examine the task-irrelevant auditory event-related potentials (tir-aERPs) as MWL indicators. A working memory task (verbal n-back) and a visuo-motor task (multi-attribute task battery, MATB), both with two difficulty levels (easy and hard), were used in the experiment, along with task-irrelevant auditory probes that did not need any response from the participants. EEG was recorded from ten participants when they were performing the tasks. The tir-aERPs elicited by the auditory probes were extracted and analyzed. The results show that the amplitudes of N1, early P3a (eP3a) and the late reorienting negativity (RON) significantly decreased with the increasing MWL in both n-back and MATB. Task type has no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERPs features. These results suggest that the tir-aERPs are potentially more constant MWL indicators across very different task types. Therefore, the tir-aERPs should be taken into consideration in future task-independent MWL monitoring studies.

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments.

Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload.Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system.Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT).Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR.Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.

Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling

In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation of working memory. However, the individual difference of the EEG feature distribution may impair the machine-learning based MW classifier. In this paper, we employed a fast-training neural network, extreme learning machine (ELM), as the basis to build an individual-specific classifier ensemble to recognize binary MW. To improve the diversity of the classification committee, heterogeneous member classifiers were adopted by fusing multiple ELMs and Bayesian models. Specifically, a deep network structure was applied in each weak model aiming at finding informative EEG feature representations. The structure of hyper-parameters of the proposed heterogeneous ensemble ELM (HE-ELM) was then identified and then its performance was compared against several competitive MW classifiers. We found that the HE-ELM model was superior for improving the individual-specific accuracy of MW assessments.

ERGONOMIA, CARGA MENTAL DE TRABALHO, RISCOS E PREVENÇÃO DE ACIDENTES

Em uma pesquisa descritiva e de estudo de campo, buscou-se avaliar quais indicadores de carga mental (CM) de trabalho são predominantes para a produção de acidentes em uma tarefa de microtomia podendo representar fonte de sofrimento psíquico. O suporte metodológico da pesquisa envolveu a elaboração de um questionário adaptado do método L.E.S.T.

Assessing visual control activities in ceramic tile surface defect detection: an eye-tracking study

Digital cameras and image processing algorithms may be helpful in inspection and classification of ceramic tiles in a production line. However, worker's decision making capability and ability to tolerate some type of defects are the main reasons for several firms to still rely on human visual inspection. Further, it is believed that the investment and maintenance costs of the automated systems may be higher than labour costs. This study considers a ceramic tile line where workers are assigned to identify tile surface defects. Main aim is to attract attention to differences between novice and expert workers in terms of visual scanning performance and mental workload indicators that result from high concentration during visual inspection. A mobile type eye-tracker is used to record the data for duration of fixation and number of fixations to determine fatigue that arises over a period of working time. Data are analysed and it is concluded that the eye tracking systems have a potential to identify human related problems during visual inspection.

Cardiovascular physiology predicts learning effects in a serious game activity

In a study on learning in serious games, 45 players were tested for topic-comprehension by a questionnaire administered before and after solo-playing of the serious game Peacemaker (Impact Games 2007), during which their psychophysiological signals were measured. Play lasted for 1 h, with a break at half time. The questionnaire was divided into two parts, with fixed and open questions respectively. We use the Bloom taxonomy to distinguish levels of difficulty in demonstrated learning - with the first five levels assigned to fixed questions - and gain scores to measure actual value of demonstrated learning. We present the analysis of the psychophysiology recorded during game play and its relationship to learning scores. The Heart Rate Variability (HRV) (an indicator of mental workload) and interaction between HRV and electromyography of Orbicularis Oculi (an indicator of positive affect) significantly predicted the learning results at certain levels of difficulty. Results indicate that increased working-memory related mental workload in support of on-task attention aids learning at these levels.

Effects of different physical workload parameters on mental workload and performance

The design and evaluation of an occupational task should include an assessment of mental workload, since excessive levels of mental workload can cause errors or delayed information processing. Physically demanding work that is performed concurrently with a cognitive task may impact mental workload by impairing mental processing or decreasing performance. The primary objective of this study was to determine whether there is a differential effect of various types of physical activity on both mental workload and cognitive performance. Objective and subjective assessment tools (heart rate variability and visual analog scale) were used as indicators of mental workload, while correct responses during an arithmetic task reflected levels of performance. Thirty participants (ages 18–24 years) performed a combination of tasks inducing both physical and mental workload. Type of physical effort, frequency of movement, and force exertion level were manipulated to alter the workload associated with the physical activity. Changes in subjective ratings generally corresponded to changes in both performance on the arithmetic task and objective mental workload assessment. Some discrepancies occurred at the highest physical force exertion level as participants perceived an increase in effort to maintain the same level of performance. Further research is needed to determine the force exertion threshold, beyond which the physical effort required interferes with mental workload and/or cognitive performance. Relevance to industry Technological advancements have increased the requirement for many workers to execute cognitive tasks concurrently with physical activity. When designing and evaluating such situations it is important to determine the interactive effects of these activities. A simple, uni-dimensional tool is suggested as a screening tool to identify situations requiring excessive or increased mental workload that many degrade performance or place additional stress on the individual.

The EEG indicators of mental workload in pilots during the dynamic flight simulation on the human centrifuge

The EEG indicators of mental workload in pilots during the dynamic flight simulation on the human centrifuge

Sensitivity and Diagnosticity of the 0.1-Hz Component of Heart Rate Variability as an Indicator of Mental Workload

We investigated whether the 0.1-Hz component of heart rate variability (HRV) allows one to discriminate among levels of mental work stress induced by different types of tasks (diagnosticity) as well as among those induced by different levels of difficulty (sensitivity). Our 14 participants were presented 14 tasks of the Advisory Group for Aerospace Research and Development Standardized Tests for Research with Environmental Stressors battery in a repeated-measures design. Sufficient sensitivity was obtained for a discrimination between work and rest, but we found no support for a more fine-grained sensitivity. Concerning diagnosticity, only the grammatical reasoning task could be discriminated from all other tasks, indicating for this task a level of mental strain comparable to rest, which was in contrast with the results both for perceived difficulty and performance. We propose that HRV is an indicator for time pressure or emotional strain, not for mental workload, given that it seems to allow discrimination between tasks with and without pacing. Application of this research argues against using HRV as a measure of mental and especially cognitive workload, particularly where system safety or occupational risks may be at stake (e.g., when evaluating operator tasks or interface design in control room operations).