Publication Hub

Classification of driver cognitive load based on physiological data: Exploring recurrent neural networks

In-vehicle systems can lead to high cognitive load that impairs driving performance. Interfaces that can detect and adapt to cognitive load accordingly may alleviate these effects. Previous research explored machine learning models to classify drivers’ cognitive load based on physiological signals but most conducted training and testing on data from the same participants (i.e., within-driver partitioning), which raises generalizability and practical feasibility concerns. In this paper, we explored the performance of widely-used models by training and testing them on data from different subjects (i.e., across-drivers partitioning), and further compared them with a more recent model that is effective for time-series data, the recurrent neural network (RNN). A driving simulator dataset was used to classify 2 levels of cognitive load (external cognitive secondary task vs. no task). All models performed better with within-driver partitioning. RNN outperformed other models with mean accuracies of 88.1% and 85.6% with within-driver and across-drivers partitioning, respectively.

Simulator

Software

4 versions available

Classification of driver cognitive load: Exploring the benefits of fusing eye-tracking and physiological measures

In-vehicle infotainment systems can increase cognitive load and impair driving performance. These effects can be alleviated through interfaces that can assess cognitive load and adapt accordingly. Eye-tracking and physiological measures that are sensitive to cognitive load, such as pupil diameter, gaze dispersion, heart rate (HR), and galvanic skin response (GSR), can enable cognitive load estimation. The advancement in cost-effective and nonintrusive sensors in wearable devices provides an opportunity to enhance driver state detection by fusing eye-tracking and physiological measures. As a preliminary investigation of the added benefits of utilizing physiological data along with eye-tracking data in driver cognitive load detection, this paper explores the performance of several machine learning models in classifying three levels of cognitive load imposed on 33 drivers in a driving simulator study: no external load, lower difficulty 1-back task, and higher difficulty 2-back task. We built five machine learning models, including k-nearest neighbor, support vector machine, feedforward neural network, recurrent neural network, and random forest (RF) on (1) eye-tracking data only, (2) HR and GSR, (3) eye-tracking and HR, (4) eye-tracking and GSR, and (5) eye-tracking, HR, and GSR. Although physiological data provided 1%–15% lower classification accuracies compared with eye-tracking data, adding physiological data to eye-tracking data increased model accuracies, with an RF classifier achieving 97.8% accuracy. GSR led to a larger boost in accuracy (29.3%) over HR (17.9%), with the combination of the two factors boosting accuracy by 34.5%. Overall, utilizing both physiological and eye-tracking measures shows promise for driver state detection applications.

Eye Tracking Glasses

Simulator

7 versions available

Classification of flight phases based on pilots’ visual scanning strategies

Eye movements analysis has great potential for understanding operator behaviour in many safety-critical domains, including aviation. In addition to traditional eye-tracking measures on pilots’ visual behavior, it seems promising to incorporate machine learning approaches to classify pilots’ visual scanning patterns. However, given the multitude of pattern measures, it is unclear which are better suited as predictors. In this study we analyzed the visual behaviour of eight pilots, flying different flight phases in a moving-base flight simulator. With this limited dataset we present a methodological approach to train linear Support Vector Machine models, using different combinations of the attention ratio and scanning pattern features. The results show that the overall accuracy to classify the pilots’ visual behaviour in different flight phases, improves from 51.6% up to 64.1% when combining the attention ratio and instrument scanning sequence in the classification model.

Eye Tracking Glasses

Simulator

4 versions available

Designing Attention—Research on Landscape Experience Through Eye Tracking in Nanjing Road Pedestrian Mall (Street) in Shanghai.

This study explores the impact of remote work on employee productivity and satisfaction. We conducted a survey with 500 participants from various industries. The findings suggest a positive correlation between remote work flexibility and improved work-life balance.

Eye Tracking Glasses

Software

2 versions available

Does a faster takeover necessarily mean it is better? A study on the influence of urgency and takeover-request lead time on takeover performance and safety

During conditionally automated driving, drivers are sometimes required to take over control of the vehicle if a so-called takeover request (TOR) is issued. TORs are generally issued due to system limitations. This study investigated the effect of different urgency scenarios and takeover-request lead times (TORlts) on takeover performance and safety. The experiment was conducted in a real vehicle-based driving simulator. Manual driving, 7-second TORlt and 5-second TORlt were each tested. Participants experienced three progressively urgent driving scenarios: one cut-in scenario and two obstacle-avoidance scenarios. The results indicate that the TORlt significantly affected takeover performance and safety. Within a certain range, the longer the TORlt, the safer the takeover. However, while takeover reaction time depended mainly on the length of the TORlt and was not significantly related to other factors, such as workload, greater workloads that were caused by the TORlt were associated with shorter reaction times and decreased safety. This is evidence that the reaction time should not be used as the preferred indicator to evaluate takeover performance and safety. Indicators, such as workload, minimum TTC, feature point distribution position and slope of the obstacle avoidance trajectory, can better measure and evaluate takeover performance and safety. This study can provide data support for takeover safety evaluation of conditionally automated driving.

Eye Tracking Glasses

Simulator

4 versions available

Does age matter? Using neuroscience approaches to understand consumers’ behavior towards purchasing the sustainable product online

In recent years, online shopping platforms have displayed more sustainable products to attract consumer attention. Understanding the effect of age on online shopping patterns can provide a broader understanding of the critical role of consumer attention. Physiological measures can explain consumers’ responses to features of online shopping websites and help these companies understand the decision-making process of consumers by using neuroscience-integrated tools. When consumers browse and shop on a platform, their eyes constantly move, effectively scanning the area of interest to capture information. This study attempts to evaluate the impact of consumer age on psychological and physiological responses to online shopping platforms by using eye tracking, EEG recordings, and FaceReader software. Eye tracker data on the average duration and number of fixations and saccades indicated that the older group had fewer eye movements than the younger group. The temporal and frontal cortices of the younger and older groups showed differences in EEG activity. The research also analyzed the faces of younger and older adults using FaceReader software; the main differences occured in the happy, surprised, and neutral expressions observed. This study enhances our understanding of the psychology and behavior of younger and older people in neuromarketing research, combining noninvasive physiological and neuroscience methods to present psychological data.

Eye Tracking Glasses

Software

7 versions available

Does gender affect the driving performance of young patients with diabetes?

Recent evidence suggests that poor glycemic control among young patients with type 1 diabetes mellitus (T1DM) has negative cognitive and physical effects, whose extent is gender-dependent. For example, female patients with diabetes present more physical and cognitive limitations than male patients in terms of cognitive adjustment, quality of decision making, and functioning. Studies about traffic safety report that diabetic drivers are at increased risk of being involved in road crashes, especially when driving in a state of hypoglycemia under which their blood glucose level is too low. We have recently demonstrated that acute hyperglycemia (when the blood glucose level is too high) can also lead to poor driving performance among T1DM young adult patients. Against this background, the objective of the present study was to find out whether gender affects the driving performance of young drivers with diabetes. Twenty-six T1DM drivers participated in a counterbalanced crossover experiment. While being monitored by an eye tracker, they drove a driving simulator and twice navigated through the nine hazardous scenarios: once under a normal blood glucose (euglycemia) level and once high blood glucose (hyperglycemia) level. The first main result is that young female drivers are more affected by diabetes than young male drivers, regardless of momentary glycemic changes. The second main result is that poor glycemic control substantially deteriorates hazard perception and driving performance of young males with diabetes. Thus, it is argued that an uncontrolled state of a high blood glucose level may be more hazardous for young males with diabetes since it negatively impacts their driving performance.

Eye Tracking Glasses

Simulator

7 versions available

EOG-based human–computer interface: 2000–2020 review

Electro-oculography (EOG)-based brain–computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users’ intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user’s communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user’s intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries.

Eye Tracking Glasses

Simulator

13 versions available

Evaluation of a dynamic blocking concept to mitigate driver distraction: three simulator studies

In recent years, the number and complexity of in-vehicle infotainment systems has been steadily increasing. While these systems certainly improve the driving experience, they also increase the risk for driver distraction. International standards and guidelines provide methods of measuring this distraction along with test criteria that help automakers decide whether an interface task is too distracting to be used while driving. Any specific function failing this test should therefore be locked out for use by the driver. This study implemented and tested a dynamic approach to this blocking by algorithmically reacting to driver inputs and the pace of the interaction in order to prevent drivers from having prolonged or too intense sequences of in-vehicle interactions not directly related to driving. Three simulated driving experiments in Germany and the United States were conducted to evaluate this dynamic function blocking concept and also cater for differences in the status quo of either no blocking or static blocking. The experiments consisted of a car following scenario with various secondary interface tasks and always included a baseline condition where no blocking occurred as well as an implementation of the dynamic function blocking. While Experiments 1 and 3 were aimed at collecting and analyzing gaze and driving data from more than 20 participants, Experiment 2 focused on the user experience evaluation of different visual feedback implementations from 13 participants. The user experience as rated by these participants increased throughout the course of all three studies and helped further improve both the concept and feedback design. In the experiments the total glance time towards the road was significantly higher in the dynamic function blocking condition compared to the baseline, already accounting for the increase in total task time inherent to the dynamic condition. Participants developed two strategies of interacting with the dynamic function blocking. They either operated at their normal baseline speed and incurred task blockings or operated slower to avoid the blockings. In the latter strategy, participants chunked their interactions into smaller steps with the present data suggesting that they used the pauses in between chunks to look back onto the road ahead. Theoretical and practical implications of this first evaluation of a dynamic function blocking concept are discussed.

Simulator

Software

6 versions available

Evaluation of students’ gaze patterns, diagnosis speed and diagnosis accuracy when interpreting clinical findings

Time and speed are vital aspects of clinical diagnosis decision-making. This study aimed to investigate the gaze patterns, diagnosis speed and accuracy with and without the assistance of clinical history while interpreting clinical findings. This cross-sectional study employed convenience sampling to recruit 28 normally sighted final year students with ongoing clinical training. Each student was shown six clinical findings, half of which accompanied a brief clinical history in prose, and the other half were not. First, the clinical history was presented to be read by the participants, followed by providing clinical findings regarding fundus picture images. The participants were asked to make a diagnosis based on a clinical finding presented to them. The Dikablis eye tracker was used to record and track the gaze patterns during the treatment. The assessment had no time restriction, and the gaze patterns (number of fixations, fixation duration, number of saccadic, and saccadic angles) were retrieved from theD-Lab software. Diagnostic speed was calculated based on the time taken for the students to provide a clinical diagnosis. Diagnosis accuracy was the score of correct or incorrect of the given diagnosis. Comparison of gaze patterns in interpreting clinical findings with clinical history and without clinical history showed no statistically significant difference for all gaze patterns including the number of fixation (p=0.20), fixation durations (p=0.98), number of saccadic (p=0.33) and saccadic angle (p=0.77). There was also no statistically significant difference in both diagnosis accuracy (p=0.14) and diagnosis speed (p= 0.20) between both conditions. However, there was a strong correlation between the number of fixations and diagnosis speed with (r = 0.708, p < 0.05) and without (r = 0.618, p < 0.05) clinical history. A moderate correlation was found between the number of saccades and diagnosis speed with (r = 0.578, p < 0.05) and without (r = 0.424, p < 0.05) clinical history. In conclusion, a brief clinical history does not appear to influence the gaze patterns, diagnosis speed and accuracy in evaluating the clinical findings. However, the gaze patterns highly correlated with the diagnosis speed in clinical decision-making. These findings indicate cognitive processing during clinical decision-making, which might benefit clinical educators in enhancing the clinical teaching approach and quality.

Eye Tracking Glasses

Software

3 versions available

Company

Publication Hub

Filter menu

Classification of driver cognitive load based on physiological data: Exploring recurrent neural networks

Classification of driver cognitive load: Exploring the benefits of fusing eye-tracking and physiological measures

Classification of flight phases based on pilots’ visual scanning strategies

Designing Attention—Research on Landscape Experience Through Eye Tracking in Nanjing Road Pedestrian Mall (Street) in Shanghai.

Does a faster takeover necessarily mean it is better? A study on the influence of urgency and takeover-request lead time on takeover performance and safety

Does age matter? Using neuroscience approaches to understand consumers’ behavior towards purchasing the sustainable product online

Does gender affect the driving performance of young patients with diabetes?

EOG-based human–computer interface: 2000–2020 review

Evaluation of a dynamic blocking concept to mitigate driver distraction: three simulator studies

Evaluation of students’ gaze patterns, diagnosis speed and diagnosis accuracy when interpreting clinical findings

Explore Cutting-Edge Research in Human Factors and Ergonomics