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Total results: 49

A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks

Year: 2022

Authors: W Li, R Tan,Y Xing,G Li,S Li, G Zeng, P Wang

Human emotions are integral to daily tasks, and driving is now a typical daily task. Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver’s viewpoints on driving scenarios that induce different emotions. For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks. The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. Moreover, The PPB-Emo dataset will also benefit human emotion research in other daily tasks.

Eye Tracking Glasses
Software

10 versions available

Assessing the impact of driver advisory systems on train driver workload, attention allocation and safety performance

Year: 2022

Authors: VJMP Verstappen, EN Pikaar, RGD Zon

Netherlands Railways has developed driver advisory systems (DAS) to provide the train driver with route context information and coasting advice in order to benefit punctuality and energy efficiency. However, the impact of these DAS on human factors aspects and safety performance is unclear. The current study assesses the impact of two DAS levels (route context information and coasting advice) on mental workload, attention allocation and safety performance, using eye tracking, a subjective mental workload rating scale (RSME) and simulator data. The overall findings suggest that the application of DAS levels has no negative impact on safety performance and attention allocation towards the trackside compared to a control condition with static timetable information. Furthermore, safety performance benefits significantly from DAS with route context information. DAS were originally developed to benefit punctuality and energy efficiency goals. This study implicates that DAS can also benefit safety performance. The current study found that DAS could decrease workload when the functionalities meet the requirements of the situation. The possible presence of mental underload and its effect on driving performance should be taken into consideration when implementing DAS. It is essential in the development of DAS that it meaningfully enriches the train driving task in stead of simply increasing mental workload.

Simulator
Software

7 versions available

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

Year: 2022

Authors: D He, Z Wang,EB Khalil,B Donmez

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

Year: 2022

Authors: V Peysakhovich,W Ledegang,M Houben

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

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

Year: 2022

Authors: MC Chiang, C Yen, HL Chen

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

How is emotional resonance achieved in storytellings of sadness/distress?

Year: 2022

Authors: C Rühlemann

Storytelling pivots around stance seen as a window unto emotion: storytellers project a stance expressing their emotion toward the events and recipients preferably mirror that stance by affiliating with the storyteller’s stance. Whether the recipient’s affiliative stance is at the same time expressive of his/her emotional resonance with the storyteller and of emotional contagion is a question that has recently attracted intriguing research in Physiological Interaction Research. Connecting to this line of inquiry, this paper concerns itself with storytellings of sadness/distress. Its aim is to identify factors that facilitate emotion contagion in storytellings of sadness/distress and factors that impede it. Given the complexity and novelty of this question, this study is designed as a pilot study to scour the terrain and sketch out an interim roadmap before a larger study is undertaken. The data base is small, comprising two storytellings of sadness/distress. The methodology used to address the above research question is expansive: it includes CA methods to transcribe and analyze interactionally relevant aspects of the storytelling interaction; it draws on psychophysiological measures to establish whether and to what degree emotional resonance between co-participants is achieved. In discussing possible reasons why resonance is (not or not fully) achieved, the paper embarks on an extended analysis of the storytellers’ multimodal storytelling performance (reenactments, prosody, gaze, gesture) and considers factors lying beyond the storyteller’s control, including relevance, participation framework, personality, and susceptibility to emotion contagion.

Eye Tracking Glasses
Software

6 versions available

Investigating the Performance of Sensor-Driven Biometrics for the Assessment of Cognitive Workload

Year: 2022

Authors: EK MacNeil

This study investigates the performance of sensor-driven biometrics for the assessment of cognitive workload. Traditional methods for evaluating cognitive workload often rely on subjective self-assessments or take significant time and resources to administer and interpret. Sensor-driven biometrics, such as heart rate variability, skin conductance, and eye tracking, offer a potential alternative. By continuously and non-invasively monitoring physiological responses, these biomarkers can provide real-time insights into cognitive workload. Understanding the relationship between biometric data and cognitive workload can improve efficiency and effectiveness in environments where cognitive demands fluctuate. This research explores the viability of various biometric sensors and analytical techniques to accurately measure and interpret cognitive workload.

Eye Tracking Glasses
Software

3 versions available

The discussion of the relationship between lighting and music in a lighting show

Year: 2022

Authors: K Xu, Y Wang,Y Jin, J Ju

An experiment based on physiological parameters and subjective emotional changes is designed to analyze the sound-light fusion effects. The different effects caused by music in major and minor keys is studied. Three colorful lighting with main wavelength at 623, 537, 445 nm, respectively, as well as two white lighting with 3000 K CCT and 6000 K CCT are evaluated in the same process. Results show that the physiological response to music is stronger than that of the lighting stimulus. Music plays a major role in the sound-light fusion environment, and the physiological perception under dual-factor stimulation is stronger than that of single factor. Moreover, music and light interact with each other when they stimulate emotions at the same time. The superposition of two positively related factors can strengthen the effects on emotion. The studies may give suggestions to the designers when they design a lighting show with music or an immersive and interactive lighting project.

Eye Tracking Glasses
Software

2 versions available

Comparative study on differences in user reaction by visual and auditory signals for multimodal eHMI design

Year: 2021

Authors: S Ahn, D Lim, B Kim

Autonomous vehicles (AV) from level 4 to level 5 will drive in traffic in a few years. The interaction between AVs and other road users could be supported by external human-machine interfaces (eHMIs). eHMIs have been suggested in various formats so far. In this study, an experiment was carried out to compare differences between visual and auditory signals. It assumed specific situations in which the AV is close to a pedestrian to assess the types of response, reaction speed, and warning. It was conducted with the Wizard of Oz technique, and individual experimental data from 18 participants were measured and analyzed. Research showed that a combination of a visual and auditory interface is most effective in understanding information. Also, auditory signals are advantageous in cognitive response in most cases, and such warnings were evaluated more highly. Therefore, it is required to consider an appropriate multimodal design when pedestrians need to pay attention.

Eye Tracking Glasses
Simulator

2 versions available

Driver cognitive load classification based on physiological data—case study 7

Year: 2021

Authors: D He,M Risteska,B Donmez, K Chen

Understanding the driver's cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algorithms on their ability to automatically identify elevated cognitive load in drivers. The study involves the collection of physiological and driving performance data from drivers in the field, and the application of various classification models to this data. The authors aim to determine which algorithms are most effective at recognizing periods of high cognitive load, with the ultimate goal of using this insight to improve driver safety and performance through adaptive vehicle systems.

Eye Tracking Glasses
Simulator

4 versions available