Driver's mental workload prediction model based on physiological indices

Evaluation of physiological responses to mental workload in n-back and arithmetic tasks

Working memory tasks, such as n-back and arithmetic tasks, are frequently used in studying mental workload. The present study investigated and compared the sensitivity of several physiological measures at three levels of difficulty of n-back and arithmetic tasks. The results showed significant differences in fixation duration and pupil diameter among three task difficulty levels for both n-back and arithmetic tasks. Pupil diameters increase with increasing mental workload, whereas fixation duration decreases. Blink duration and heart rate (HR) were significantly increased as task difficulty increased in the n-back task, while root mean square of successive differences (RMSSD) and standard deviation of R-R intervals (SDNN) were significantly decreased in the arithmetic task. On the other hand, blink rate and Galvanic Skin Response (GSR) were not sensitive enough to assess the differences in task difficulty for both tasks. All significant physiological measures yielded significant differences between low and high task difficulty except for SDNN.Practitioner summary: This study aimed to assess the sensitivity levels of several physiological measures of mental workload in n-back and arithmetic tasks. It showed that pupil diameter was the most sensitive in both tasks. This study also found that most physiological indices are sensitive to an extreme change in task difficulty levels.

How flight experience impacts pilots' decision-making and visual scanning pattern in low-visibility approaches: preliminary evidence from eye tracking

The visual approach is the most accident-prone phase of a flight, especially in low-visibility conditions. This preliminary study aimed to examine the effects of flight experience on pilots' decision-making and visual scanning pattern in low-visibility approaches. Twenty pilots were separated into two groups based on their flight experience and completed the high- and low-visibility approaches in balanced order using a high-fidelity flight simulator. Pilots' mental workload and visual scanning patterns were recorded via an eye tracker. The results showed that, compared to less flight-experienced pilots (20%, 3/15), experienced pilots (80%, 4/5) were more likely to make go-around decisions in the low-visibility approaches. Furthermore, they exhibited a more flexible and adaptable visual scanning pattern by quickly shifting their attention, as evidenced by decreased fixations and increased saccades. These findings suggest that the integration of visual scanning strategy and training solution with a marginally meteorological approach may enhance decision-making safety for novice pilots.

A systematic review on the influence factors, measurement, and effect of driver workload

Driver workload (DWL) is an important factor that needs to be considered in the study of traffic safety. The research focus on DWL has undergone certain shifts with the rapid development of scientific and technological advancements in the field of transportation in recent years. This study aims to grasp the state of research on DWL by both bibliometric analysis and individual critical literature review. The knowledge structure and development trend are described using bibliometric analysis. The knowledge mapping method is applied to mine the available literature in depth. It is discovered that one of the current research focus on DWL has shifted towards investigating its application in the field of autonomous driving. Subjective questionnaires and experimental tests (including both simulation technology and field study) are the main approaches to analyze DWL. An individual critical literature review of the influencing factors, measurement, and performance of DWL is provided. Research findings have shown that DWL was highly impacted by both intrinsic (e.g., age, temperament, driving experience) and external factors (e.g., vehicles, roads, tasks, environments). Scholars are actively exploring the combined effects of various factors and the level of vehicle automation on DWL. In addition to assess DWL by using subjective measures or physiological parameter measures separately, studies have started to improve classification accuracy by combining multiple measurement methods. Safety thresholds of DWL are not sufficiently studied due to the various interference items corresponding to different scenarios, but it is expected to quantify the DWL and find the threshold by establishing assessment models considering these intrinsic and external multiple-factors simultaneously. Driver or vehicle performance indicators are controversial to measure DWL directly, but they were suitable to reflect the impact of DWL in different driving conditions.

Pilots' mental workload variation when taking a risk in a flight scenario: a study based on flight simulator experiments

Pilots' operation behavior in flight is associated with their mental state variables such as workload, situation awareness, stress, etc. The objective of this study was to investigate the dynamic process of mental workload for pilots who perform a risky flight task in simulated scenarios. Two empirical experiments were conducted to address this issue. In experiment one, 19 trainee pilots divided into high-risk and low-risk groups performed a target-search task in a low-altitude visual flight. The results showed a statistically significant interaction between groups and segments for heart rate variability (HRV). The same pattern of physiological results was replicated among participants in experiment two, in which 19 airline pilots completed an approach with low visibility. These findings highlighted the relationship between mental workload variation and risk-taking behavior, which could be considered in improving pilot selection and training to improve flight safety.

Exploring the effects of road type on drivers' eye behavior and workload

The number of traffic accidents is increasing every year. This study researched the effect of road type on driver's workload and eye behavior. The results showed that the road type has an effect on workload, pupil diameter, fixation rate and number of fixations. The workload, pupil diameter, fixation rate and number of fixations have a positive correlation with the complexity of road conditions. The research on visual attention area found that the driver's attention area during driving includes the rear-view mirror, left mirror, right mirror and middle area of the interface. In addition, the search range in the horizontal direction is largest under the urban road condition. Drivers focus on the ahead situation in the expressway condition, and the driver focuses on the opposite and front road in the rural road condition. The research results can be used for drivers' training in the future.

Cognitive Analyses for Interface Design Using Dual N-Back Tasks for Mental Workload (MWL) Evaluation

In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study's contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user-system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants' mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.

Effects of mental workload on manufacturing systems employees: A mediation causal model

BACKGROUND

Although some research has been done in the Mexican manufacturing industry regarding mental workload, none has explored its association with physical fatigue, body weight gain, and human error simultaneously.

OBJECTIVE

This research examines the association between mental workload and physical fatigue, body weight gain, and human error in employees from the Mexican manufacturing systems through a mediation analysis approach.

METHODS

A survey named Mental Workload Questionnaire was developed by merging the NASA-TLX with a questionnaire containing the mental workload variables mentioned above. The Mental Workload Questionnaire was applied to 167 participants in 63 manufacturing companies. In addition, the mental workload was used as an independent variable, while physical fatigue and body weight gain were mediator variables, and human error was a dependent variable. Six hypotheses were used to measure the relationships among variables and tested using the ordinary least squares regression algorithm.

RESULTS

Findings indicated that mental workload significantly correlates with physical fatigue and human error. Also, the mental workload had a significant total association with human error. The highest direct association with body weight gain was provided by physical fatigue, and body weight gain had an insignificant direct association with human error. Finally, all indirect associations were insignificant.

CONCLUSION

Mental workload directly affects human error, which physical fatigue does not; however, it does affect body weight gain. Managers should reduce their employees' mental workload and physical fatigue to avoid further problems associated with their health.

Subjective and objective assessments of mental workload for UAV operations

BACKGROUND

Unmanned Aerial Vehicles (UAVs) have created safety problems for the publics. Assessments of the mental workload for UAV operations are essential to realize the causes of UAV accidents.

OBJECTIVE

To test the following hypotheses: i. mission difficulty in UAV operation affects both subjective and objective measures of mental workload; ii. mission difficulty affects number of failures in UAV operation.

METHODS

Fourteen male adults participated as UAV operators after attending a UAV training course. They performed four flight missions of different difficulty levels. During their flight missions, their heart rate and inter-beat interval (IBI) were collected. Upon completing each flight mission, the participants gave subjective ratings of mental workload using three commonly adopted assessment tools. The time of flight and number of failures in flight operations were also recorded.

RESULTS

The results showed that mission difficulty affected the scores of all three assessment tools significantly. Mission difficulty also affected number of failures and IBI significantly. The scores of the three assessment tools were highly correlated (ρ= 0.7 to 0.83, p < 0.001) with one another. The results of the three subjective ratings were also consistent with that of the IBI data.

CONCLUSIONS

High mental workload in UAV operation could lead to poor flight performance.

Development of an Eye Responses-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.

Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data

The aim of this study is to investigate the usefulness of the anomaly detection method by one-class support vector machine (OCSVM) for the evaluation of mental workload (MWL) during automobile driving. Twelve students (six males and six females) participated. The participants performed driving tasks with a driving simulator (DS) and the N-back task that was used to control their MWL. The N-back task had five difficulty levels from "none" to "3-back." Eye and head movements were measured during the DS driving. Results showed that the standard deviation (SD) of the gaze angle, SD of eyeball rotation angle, share rate of head movement, and blink frequency had significant correlations with the task difficulty. The decision boundary of OCSVM could detect 95% of high MWL state (i.e., "3-back" state). In addition, the absolute value of the distance from the decision boundary increased with the task difficulty from "0-back" to "3-back."

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.

Effect of attentional focus levels on spontaneous eyeblink rate in horses

OBJECTIVE

Spontaneous eyeblink rate (SEBR) is inversely proportional to the level of attentional focus in humans. The objective of this study was to evaluate the effects of attentional focus on SEBR in horses.

PROCEDURES

Twenty client-owned horses (2.5-23 years of age, five mares, 15 geldings) were evaluated. A GoPro HERO5 Session™ video recorder was attached to the halter and directed toward the eyes. Horses were acclimated for two hours before study commencement. Horses were filmed in five scenarios requiring different levels of attention: alone in a stall ("stall"), stall with an observer ("person"), standing in an arena ("stand"), walking in an arena ("walk"), and trotting in arena ("trot"). Each of the five scenarios was filmed for ten minutes. Video recordings were reviewed, and SEBR (incomplete, complete, and total blinks) was measured for both eyes in each scenario.

RESULTS

The median (25%,75%) total SEBRs (blinks/min) were as follows: STALL: OD-16.6 (12.4, 24.3), OS-18.8 (11.5, 26.9); PERSON: OD-17.7 (14.6, 24.2), OS-17.4 (13.9, 24.4); STAND: OD-18.7 (15.1, 24.4), OS-19.4 (16.2, 24.7); WALK: OD-40.1 (34.1, 44.8), OS-40.6 (33.8, 44.7); and TROT: OD-39.7 (30.3, 44.6), OS-39.4 (28.6, 45.0). The total SEBRs at walk and trot were significantly greater than SEBR in stationary scenarios.

CONCLUSIONS

Despite requiring increased levels of attentional focus, activities involving motion caused increased SEBR in horses. We hypothesize the need for additional tear distribution due to tear evaporation (associated with air movement over the corneas) prevailed over the level of attentional focus to increase the SEBR.

Human-based dynamics of mental workload in complicated systems

As a dynamic system in which different factors affect human performance via dynamic interactions, mental workload needs a dynamic measure to monitor its factors and evidence in a complicated system, an approach that is lacking in the literature. The present study introduces a system dynamics-based model for designing feedback mechanisms related to the mental workload through literature review and content analysis of the previous studies. A human-based archetype of mental workload was detected from the data collection process. The archetype is presented at various stages, including dynamic theory, behavior over time, leverage points and model verification. The real validation of the dynamic model was confirmed in an urban train simulator. The dynamic model can be used to analyze the long-term behavior of the mental workload. Decision-makers can benefit from the developed archetypes in evaluating the dynamic impact of their decisions on accident prevention in the complicated systems.

Resting Cardiac Vagal Tone is Associated with Long-Term Frustration Level of Mental Workload: Ultra-short Term Recording Reliability

Excessive mental workload represent a critical risk factor for workplace accidents. Heart rate variability (HRV) is a non-invasive low cost electrophysiological autonomic biomarker related to emotional and cognitive regulation. Several studies report that mental overload impairs parasympathetic-mediated HRV indices (e.g. rMSSD). However, the influence of resting state HRV as a predictor of long-term mental workload impairments remains unknown. Thirty participants (22 males; 8 females) had their HRV measured (5-min period) before performing the number search task. After the task, the mental load was accessed by the NASA-TLX questionnaire. A simple linear regression model between HRV and NASA-TLX dimensions showed that resting state rMSSD is associated to physical demand (ND-2, R² = 0.143, p = 0.03) and frustration level (ND-6, R² = 0.175, p = 0.02) dimensions of mental workload. The comparison between 1 and 5-min epochs suggests that regression models remain reliable even using the ultra-short term HRV (< 1 min) recording values (R² values from 0.11 to 0.15 for ND-2 and R² values from 0.16 to 0.19 for ND-6). These results suggest that resting state HRV is associated to long-term effects of mental workload on physical and emotional demands. In addition, the ultra-short term HRV indices remains reliable to assess ND-2 and ND-6 dimensions of mental workload when compared to gold-standard time interval (> 5 min). The resting state cardiac autonomic tone assessment optimizes the physiological approach with a quick, non-invasive and low-cost assessment that can provide insights about mental load adjustments to prevent work-related accidents.

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

A Survey to Understand Emotional Situations on the Road and What They Mean for Affective Automotive UIs

In this paper, we present the results of an online survey (N = 170) on emotional situations on the road. In particular, we asked potential early adopters to remember a situation where they felt either an intense positive or negative emotion while driving. Our research is motivated by imminent disruptions in the automotive sector due to automated driving and the accompanying switch to selling driving experiences over horsepower. This creates a need to focus on the driver’s emotion when designing in-car interfaces. As a result of our research, we present a set of propositions for affective car interfaces based on real-life experiences. With our work we aim to support the design of affective car interfaces and give designers a foundation to build upon. We find respondents often connect positive emotions with enjoying their independence, while negative experiences are associated mostly with traffic behavior. Participants who experienced negative situations wished for better information management and a higher degree of automation. Drivers with positive emotions generally wanted to experience the situation more genuinely, for example, by switching to a “back-to-basic” mode. We explore these statements and discuss recommendations for the design of affective interfaces in future cars.