scholarly journals A Study on the Priorities of Urban Street Environment Components - Focusing on An Analysis of AOI (Area of Interest) Setup through An Eye-tracking Experiment -

2016 ◽  
Vol 25 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Sun Hwa Lee ◽  
Chang No Lee
Keyword(s):  
Eye Tracking ◽  
Urban Street ◽  
Area Of Interest ◽  
Street Environment

scholarly journals Use of machine learning to identify autism with natural social gaze behavior (Preprint)

2021 ◽  
Author(s):  
Zhong Zhao ◽  
Haiming Tang ◽  
Xiaobin Zhang ◽  
Xingda Qu ◽  
Jianping Lu
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Classification Accuracy ◽  
Autism Spectrum ◽  
Support Vector ◽  
Gaze Behavior ◽  
Linear Discriminant ◽  
Face To Face ◽  
Area Of Interest ◽  
Social Gaze

BACKGROUND Abnormal gaze behavior is a prominent feature of the autism spectrum disorder (ASD). Previous eye tracking studies had participants watch images (i.e., picture, video and webpage), and the application of machine learning (ML) on these data showed promising results in identify ASD individuals. Given the fact that gaze behavior differs in face-to-face interaction from image viewing tasks, no study has investigated whether natural social gaze behavior could accurately identify ASD. OBJECTIVE The objective of this study was to examine whether and what area of interest (AOI)-based features extracted from the natural social gaze behavior could identify ASD. METHODS Both children with ASD and typical development (TD) were eye-tracked when they were engaged in a face-to-face conversation with an interviewer. Four ML classifiers (support vector machine, SVM; linear discriminant analysis, LDA; decision tree, DT; and random forest, RF) were used to determine the maximum classification accuracy and the corresponding features. RESULTS A maximum classification accuracy of 84.62% were achieved with three classifiers (LDA, DT and RF). Results showed that the mouth, but not the eyes AOI, was a powerful feature in detecting ASD. CONCLUSIONS Natural gaze behavior could be leveraged to identify ASD, suggesting that ASD might be objectively screened with eye tracking technology in everyday social interaction. In addition, the comparison between our and previous findings suggests that eye tracking features that could identify ASD might be culture dependent and context sensitive.

Evaluation of Situations Causing Split of Attention in Multimedia Learning Environments via Eye-Tracking Method

2015 ◽  
pp. 337-367 ◽  
Author(s):  
Duygu Mutlu-Bayraktar ◽  
Servet Bayram
Keyword(s):  
Eye Tracking ◽  
Learning Environments ◽  
Visual Information ◽  
Multimedia Learning ◽  
Heat Maps ◽  
Tracking Method ◽  
Attention Effect ◽  
Area Of Interest ◽  
Video Images ◽  
Multimedia Environments

In this chapter, situations that can cause split of attention in multimedia environments were determined via eye tracking method. Fixation numbers, heat maps and area of interest of learners were analyzed. As a result of these analyses, design suggestions were determined for multimedia environments to provide focusing attention to content without split attention effect. Visual and auditory resources should be provided simultaneously. Visual information should be supported with auditory expression instead of texts. Images such as videos, pictures and texts should not be presented on the same screen. Texts provided with pictures should be presented via integration to each other instead of separate presentation of text and picture. Texts provided with videos should be presented via integration to each other instead of separate presentation of text and video. Images should be given via marking important points on images to increase attention.

scholarly journals Division of Area of Fixation Interest for Real Vehicle Driving Tests

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Qian Xu ◽  
Tang-yi Guo ◽  
Fei Shao ◽  
Xue-jiao Jiang
Keyword(s):  
Eye Tracking ◽  
Clustering Algorithm ◽  
Mean Shift ◽  
Characteristic Analysis ◽  
Novice Drivers ◽  
Area Of Interest ◽  
Visual Characteristic ◽  
Fixation Points ◽  
Tracking Device ◽  
Real Vehicle

The area of interest (AOI) reflects the degree of attention of a driver while driving. The division of AOI is visual characteristic analysis required in both real vehicle tests and simulated driving scenarios. Some key eye tracking parameters and their transformations can only be obtained after the division of AOI. In this study, 9 experienced and 7 novice drivers participated in real vehicle driving tests. They were asked to drive along a freeway section and a highway section, wearing the Dikablis eye tracking device. On average, 8132 fixation points for each driver were extracted. After coordinate conversion, the MSAP (Mean Shift Affinity Propagation) method is proposed to classify the distribution of fixation points into a circle type and a rectangle type. Experienced drivers’ fixation behavior falls into the circle type, in which fixation points are concentrated. Novice drivers’ fixation points, which are decentralized, are illustrated in the rectangle type. In the clustering algorithm, the damping coefficient λ determines the algorithm convergence, and the deviation parameter p mainly affects the number of clusters, where larger p values generate more clusters. This study not only provides the cluster type and cluster counts, but also presents the borderlines for each cluster. The findings provide significant contribution to eye tracking research.

Mapping 3D visibility in an urban street environment from mobile LiDAR point clouds

2020 ◽  
Vol 57 (6) ◽  
pp. 797-812
Author(s):  
Yi Zhao ◽  
Bin Wu ◽  
Jianping Wu ◽  
Song Shu ◽  
Handong Liang ◽  
...  
Keyword(s):  
Point Clouds ◽  
Urban Street ◽  
Street Environment

scholarly journals Visual Attention Patterns in Adults According to the Use of Things in Aided AAC VSDs: An Eye-tracking Study

2021 ◽  
Vol 26 (3) ◽  
pp. 718-726
Author(s):  
Jin Hui Lee ◽  
Ji Young Na ◽  
Su Hyang Lee ◽  
Bong Won Yi
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Target Object ◽  
Visual Scene ◽  
Partial Group ◽  
Area Of Interest ◽  
Total Fixation ◽  
Object Usage ◽  
The Difference ◽  
Tracking Technology

Objectives: This study aims to investigate patterns of visual attention on a target object in VSDs (Visual Scene Displays) when they are designed with/without an action of usage of the object. We used eye-tracking technology to evaluate how the action of usage of an object in still photographs influenced the visual attention of adults without disabilities. We tried to examine visual attention on the contents of visual scene displays (VSDs).Methods: 25 college students participated in the study. Eye-tracking technology recorded point-of-gaze while participants viewed 20 photographs. Data from eye-tracking provided information on where participants were visually fixated and paid more attention on the presented VSDs including a target object.Results: Both total fixation duration and average fixation count were statistically significant. Participants visually fixated on the target object longer and more often when the object was being used in the presented VSDs. For AOI (Area Of Interest) time of the first fixation, after analyzing only a partial group that had the data match due to the difference in gaze pattern per subject, the average AOI time of the first fixation was shown to be faster when using an object in 6 out of 10 objects.Conclusion: This study supports the inclusion of an action of an object usage in VSDs suggesting that the act of object usage can partially influence the visual attention pattern of a user.

scholarly journals Eye-tracking based quantification of the safety of human-machine interfaces of complementary protective system functions

2021 ◽  
Vol 5 (2) ◽  
pp. 1-13
Author(s):  
Julian Zehetner ◽  
Ivo Häring ◽  
Ulrich Weber ◽  
Werner Riedel
Keyword(s):  
Eye Tracking ◽  
Preliminary Test ◽  
Human Interaction ◽  
Protective Measures ◽  
Area Of Interest ◽  
Human Machine Interfaces ◽  
Test Parameters ◽  
Protective System ◽  
Visit Duration ◽  
Tracking Technology

Complementary protective measures are of increasing importance with rising degree of automation. As free robots become part of our daily life in industry, on shop floors and beyond, the overall safety of persons has to be ensured. However, assessing the reliability of complementary safety functions remains a challenge, particularly when humans are in the loop. The paper shows how to use the eye-tracking methodology to gain data for assessing the reliability of the human interaction with machine interfaces for complementary protective measures. The paper first identifies factors relevant for eye-tracking, then selects related eye tracking test parameters and finally provides a systematic procedure to assess both, in particular regarding visibility and susceptibility. The methodology is applied and the parameter selection is validated. It is found that in particular the identified and measured parameters fixation count for area of interest (AOI) and the associated average visit duration can be used to assess the factor perceptibility. The parameter deviation of fixation can thereby be used to assess usability. Based on this, a full-scale eye-tracking assessment is proposed for the reliability of the interaction of humans with the machine interfaces of supplementary protective measures. In summary, the preliminary test run execution shows that eye-tracking technology is a promising method for measuring and quantifying the human reliability when interacting with safety-related human-machine interfaces.

scholarly journals Introducing Point-of-Interest as an alternative to Area-of-Interest for fixation duration analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250170
Author(s):  
Nak Won Rim ◽  
Kyoung Whan Choe ◽  
Coltan Scrivner ◽  
Marc G. Berman
Keyword(s):  
Eye Tracking ◽  
Social Interactions ◽  
Dwell Time ◽  
Fixation Duration ◽  
Weighted Sum ◽  
Time Analysis ◽  
Point Of Interest ◽  
Area Of Interest ◽  
Points Of Interest ◽  
Alternative Approach

Many eye-tracking data analyses rely on the Area-of-Interest (AOI) methodology, which utilizes AOIs to analyze metrics such as fixations. However, AOI-based methods have some inherent limitations including variability and subjectivity in shape, size, and location of AOIs. In this article, we propose an alternative approach to the traditional AOI dwell time analysis: Weighted Sum Durations (WSD). This approach decreases the subjectivity of AOI definitions by using Points-of-Interest (POI) while maintaining interpretability. In WSD, the durations of fixations toward each POI is weighted by the distance from the POI and summed together to generate a metric comparable to AOI dwell time. To validate WSD, we reanalyzed data from a previously published eye-tracking study (n = 90). The re-analysis replicated the original findings that people gaze less towards faces and more toward points of contact when viewing violent social interactions.

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