User's Gaze Research Articles

Gaze window: A new gaze interface showing relevant content close to the gaze point

AbstractIn this paper, we propose a novel concept of the gaze window, which follows the user's gaze to show relevant information close to their gaze point on a screen space. We explore the use of a gaze window, especially for two use cases: (1) a single‐user data entry task by displaying data‐source image close to the gaze point where the data are typed in and (2) a teleconferencing setup showing a remote partner close to the gaze point. To investigate the performance of gaze window in these cases, we conducted two experiments. For the single‐user task, we compared the gaze window with a baseline non‐gaze‐window condition and found that the gaze window significantly reduced the user's gaze movement and mental effort and increased usability. In the teleconferencing task, we compared displaying partner close to gaze point in the gaze window to displaying partner at the corner of screen, when two remote users watch a shared and synchronized content together. We found that displaying the partner's face nearby the user's gaze helped the user look at the partner more frequently, and for a longer time, significantly increasing co‐presence and emotional interdependence. As a result of this research, we also presented design guidelines for gaze‐window systems.

Affective computing study of attention recognition for the 3D guide system

The eye-tracking has been widely used in multiple discipline studies in recent years. However, most of the studies focused on the analysis for static images or text but less for highly interactive operation application. In addition, the affective computing rose and development in recent years have changed completely the design of thinking pattern for the human–computer interaction. Therefore, this study hopes to integrate the affective computing into the 3D guide system which was developed for the real campus through the eye-tracking technology. The analysing user's gaze position and recognising attention emotion are according to the interest region which is stetted into the environment, and shows the feedback content corresponds to the area and the emotion. Through the most intuitive gaze analysis, the operation burden can be reduced and the user's interactive experience can be improved to achieve intuitive and user-friendly experience. The results can also apply for medical therapy on human attention training.

Collaborative Use of a Shared System Interface: The Role of User Gaze—Gaze Convergence Index Based on Synchronous Dual-Eyetracking

Gaze convergence of multiuser eye movements during simultaneous collaborative use of a shared system interface has been proposed as an important albeit sparsely explored construct in human-computer interaction literature. Here, we propose a novel index for measuring the gaze convergence of user dyads and address its validity through two consecutive eye-tracking studies. Eye-tracking data of user dyads were synchronously recorded while they simultaneously performed tasks on shared system interfaces. Results indicate the validity of the proposed gaze convergence index for measuring the gaze convergence of dyads. Moreover, as expected, our gaze convergence index was positively associated with dyad task performance and negatively associated with dyad cognitive load. These results suggest the utility of (theoretical or practical) applications such as synchronized gaze convergence displays in diverse settings. Further research perspectives, particularly into the construct’s nomological network, are warranted.

Impact of bookshelf locations using eye-tracking methodology

PurposeThis study aimed to understand better the user gaze behavior on bookshelves using eye-tracking technology.Design/methodology/approachAn eye-tracking experiment in a public library with 11 participants was performed. The impact of vertical shelf location of books on the number of times the books are looked at, the impact of horizontal location and the relationship between user behavior and location impact were examined by the findings.FindingsThe results showed that the vertical location of books has a significant impact on the number of times the books are looked at. More than 80% of the time spent looking at bookshelves was spent on books on the top to fourth rows. It was also revealed that the horizontal location of books has a little impact. Books located on the left side of shelves will be looked at significantly more often than those on the right side. No significant relationships between type of user behaviors and location impact were observed.Originality/valueThe study explored the impact of the vertical location of books on time spent looking at bookshelves using eye-tracking methodology. Few published studies do such experiments to address user gaze behavior on bookshelves. The study explored that the vertical location of books has a great impact, and horizontal location has a little impact on user gaze behavior.

Towards expert gaze modeling and recognition of a user’s attention in realtime

Abstract One of the appealing areas of expertise research is devoted to measuring the effectiveness of training programs for novices. With recent progress in eye tracking, gaze-based interaction systems recognize a user’s attention and can direct it accordingly. Moreover, dynamic visualization of an expert gaze model facilitates novice training by guiding the gaze to relevant areas. In addition, the system should be aware of realtime attention to remove an overlay that could occlude relevant information. We use an implementation of subtle gaze direction (SGD) and the simplified scanpath of a dentist to train naive participants in finding anomalies in dental radiographs. We were able to effectively direct user gaze to relevant image features without occluding the area when attention was recognized. Additionally, participants reported that the intervention was helpful for image inspection. The results of the model intervention show minimal improvements in anomaly detection, which is expected of naive subjects. We advocate that the system has the potential to be highly effective for advanced students and trainees with a certain foundation of conceptual knowledge.

Towards Adaptive Ontology Visualization — Predicting User Success from Behavioral Data

Ontology visualization plays an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability studies of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavioral data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment, as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach, where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates the feasibility of using behavioral data, such as user gaze and event logs, as real-time indicators of how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may be used to inform the adaptation of the visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis from a controlled experiment and demonstrate such predictions are not only feasible, but can also be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance. Furthermore, we demonstrate the prediction performance using several different feature sets, and report on the results generated from several notable classifiers, where a decision tree-based learning model using a boosting algorithm produced the best overall results.

Wand-Like Interaction with a Hand-Held Tablet Device—A Study on Selection and Pose Manipulation Techniques

Current hand-held smart devices are supplied with powerful processors, high resolution screens, and sharp cameras that make them suitable for Augmented Reality (AR) applications. Such applications commonly use interaction techniques adapted for touch, such as touch selection and multi-touch pose manipulation, mapping 2D gestures to 3D action. To enable direct 3D interaction for hand-held AR, an alternative is to use the changes of the device pose for 6 degrees-of-freedom interaction. In this article we explore selection and pose manipulation techniques that aim to minimize the amount of touch. For this, we explore and study the characteristics of both non-touch selection and non-touch pose manipulation techniques. We present two studies that, on the one hand, compare selection techniques with the common touch selection and, on the other, investigate the effect of user gaze control on the non-touch pose manipulation techniques.

I-VALS: Visual Attention Localization for Mobile Service Computing

Identifying and localizing the user's visual attention can enable various intelligent service computing paradigms in a mobile environment. However, existing solutions can only compute the gaze direction, but without the distance to the intended target. In addition, most of them rely on eye tracker or similar infrastructure support. This paper explores the possibility of using portal mobile devices, e.g., smartphone, to detect the visual attention of a user. i-VALS only requires the user to do one simple action to localize the intended object: gazing at the intended object and holding up the smartphone so that the object and the user's face can be simultaneously captured by the front and rear cameras. We develop efficient algorithms to obtain both the distance between the camera and user, the user's gaze direction and the object's direction from the camera. The object's location can then be computed by solving a trigonometric problem. i-VALS has been prototyped on commercial off-the-shelf (COTS) devices. The extensive experiment results show that i-VALS achieves high accuracy and small latency, effectively supporting a large variety of applications in smart environments.

Gaze-Driven Design Insights to Amplify Debugging Skills: A Learner-Centered Analysis Approach

This study investigates how multimodal user-generated data can be used to reinforce learner reflection, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize user gaze and action-based data to examine the role of a mirroring tool (i.e., Exercise View in Eclipse) in orchestrating basic behavioural regulation during debugging. The results demonstrated that students who processed the information presented in the Exercise View and acted upon it, improved their performance and achieved a higher level of success than those who failed to do so. The findings shed light on what constitutes relevant data within a particular learning context in programming using gaze patterns. Moreover, these findings could guide the collection of essential learner-centred analytics for designing usable, modular learning environments based on data-driven approaches.

3-Dimensional Calibration and Performance Evaluation Method for Pupil-labs Mobile Pupil Tracking Device

동공 추적 기술은 스마트 장치와 연계하여 사용자에게 편의를 제공할 수 있는 효율적인 정보 제공 수단으로 사용될 수 있다. 본 논문에서는 Pupil-labs에서 제작한 모바일 동공 추적 장치를 사용하여 사용자의 응시점 거리를 측정하고, 정확도와 정밀도를 분석하는 실험 수행결과를 보인다. 이를 기반으로 동공 추적 장치가 측정하는 동공의 응시점 위치와 대상 타겟과의 오차를 비교 한다. 모바일 동공 추적 장치도 한 종류의 카메라이기 때문에 사용하기 전에는 반드시 캘리브레이션 작업을 수행해야 한다. 일반적으로 사용하는 2차원 캘리브레이션 방법뿐만 아니라, 3차원 캘리브레이션 수행 방법에 대해 설명한다. 3차원 캘리브레이 션은 2차원 캘리브레이션 결과보다 높은 정확도를 갖기 위해임의의 평면을 설정하여 다양한 3차원 공간상에서 캘리브레이션을 수행하는 과정을 의미한다. 3차원 캘리브레이션의 효율성을 보이기 위해 실험결과에 대한 분석 결과를 설명한다. 또한 전반적인 장치의 사용 방법과 장치를 통해 얻을 수 있는 다양한 정보를 소개한다.Pupil tracking technologies can be used as an efficient information provider means that provides convenience to the user by connecting with a smart device. In this paper, we measure the distance of user gaze point using the pupil tracking device which produced by Pupil-labs, also shows the experimental result with analyzing accuracy and precision. Based on that the pupil gaze point location which tracked by pupil tracking device is compared with object target in terms of error. Since the mobile pupil tracking device is also one kind of camera, we have to perform the calibration before using the device. Not only generally used 2-dimensional calibration, but also 3-dimensional calibration method is explained. To get the improved accuracy of 2-dimensional calibration result, the 3-dimensional calibration set an imaginary plane and executes the calibration in various 3-dimensional spaces. To show the efficiency of 3-dimensional calibration, we analyze the experimental result. It also introduces various using methods and information that can be obtained through the device.

The use of eye-tracking methods for evaluation of responsive web application

The purpose of this article is to present the eye-tracking method in the context of evaluation of responsive web application dedicated to manage students practices. To determine usability of the application the eye-tracking method has been chosen due to its high objectivity. Using this method one can unequivocally determine the user gaze direction, which interface elements enchained his attention and which ones were completely skipped. As part of the work were prepared scenarios with tasks for the research participants, questionnaires and time of the tasks execution where measured. Collected data were presented as heat maps and scanpaths. The paper covers also actual review of bibliography of the eye-tracking area and detailed description of the eye-tracking method. What is more the paper gives description of test subject, conducted test and discusses results.

Study on Stimulus Frequency in SSVEP-Based Brain-Computer Interface Considering User's Gaze

Study on Stimulus Frequency in SSVEP-Based Brain-Computer Interface Considering User's Gaze

Gazture

We present Gazture, a light-weight gaze based real-time gesture control system on commercial tablets. Unlike existing approaches that require dedicated hardware (e.g., high resolution camera), high computation overhead (powerful CPU) or specific user behavior (keeping head steady), Gazture provides gesture recognition based on easy-to-control user gaze input with a small overhead. To achieve this goal, Gazture incorporates a two-layer structure: The first layer focuses on real-time gaze estimation with acceptable tracking accuracy while incurring a small overhead. The second layer implements a robust gesture recognition algorithm while compensating gaze estimation error. To address user posture change while using mobile device, we design a online transfer function based method to convert current eye features into corresponding eye features in reference posture, which then facilitates efficient gaze position estimation. We implement Gazture on Lenovo Tab3 8 Plus tablet with Android 6.0.1, and evaluate its performance in different scenarios. The evaluation results show that Gazture can achieve a high accuracy in gesture recognition while incurring a low overhead.

Flicker Observer Effect: Guiding Attention Through High Frequency Flicker in Images

Drawing the user's gaze to an important item in an image or a graphical user interface is a common challenge. Usually, some form of highlighting is used, such as a clearly distinct color or a border around the item. Flicker can also be very salient, but is often perceived as annoying. In this paper, we explore high frequency flicker 60 to 72 Hz to guide the user's attention in an image. At such high frequencies, the critical flicker frequency CFF threshold is reached, which makes the flicker appear to fuse into a stable signal. However, the CFF is not uniform across the visual field, but is higher in the peripheral vision at normal lighting conditions. Through experiments, we show that high frequency flicker can be easily detected by observers in the peripheral vision, but the signal is hardly visible in the foveal vision when users directly look at the flickering patch. We demonstrate that this property can be used to draw the user's attention to important image regions using a standard high refresh-rate computer monitor with minimal visible modifications to the image. In an uncalibrated visual search task, users could in a crowded image easily spot the specified search targets flickering with very high frequency. They also reported that high frequency flicker was distracting when they had to attend to another region, while it was hardly noticeable when looking at the flickering region itself.

Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations.

Selection of Optimal Frequency Bands of the Electroencephalogram Signal in Eye-brain-computer Interface

An eye-brain-computer interface (EBCI) is a hybrid system that combines properties of eye tracking systems and brain-computer interfaces, based on the analysis of the electroencephalogram (EEG). In the hybrid interface the object of interest on a screen is determined by tracking the user's gaze direction. At the same time, the EEG signal is used to detect the user's intent to give a command. This article discusses the recognition of EEG patterns that correspond to spontaneous and control gaze fixations. We propose to extract the most informative features of the EEG signal by selecting optimal frequency bands of the signal. The method is based on solving one-criterion optimization task, in which variable parameters are the boundaries of frequency bands, and the quality of the class separation acts as an objective function. To find optimal values of variable parameters we suggest using the particle swarm optimization. We evaluate the efficiency of the proposed method on EEG recordings obtained at the Kurchatov Complex of NBICS Technologies for users working with a hybrid interface. It is shown that for all users our method improves classification accuracy in comparison with other methods of feature extraction.

The Importance of E-mail Marketing in E-commerce

Online marketing is an integral part of e-commerce nowadays and includes many different ways of company's presentation, such as e-mail marketing, content marketing, social media, affiliate marketing and so on. The aim of the article is to highlight the importance of e-mail marketing due to the fact, it is considered as one of the most effective communication tools. The part of the article are also fundamental metrics used in e-mail marketing such as delivery rate, open rate and click through rate. In the end of the article are recommendations and suggestions for creating of successful e-mail. The eyetracking technology was also used to analyze the gaze of Internet users at mailbox.

Gaze leading is associated with liking

Gaze plays a pivotal role in human communication, especially for coordinating attention. The ability to guide the gaze orientation of others forms the backbone of joint attention. Recent research has raised the possibility that gaze following behaviors could induce liking. The present study seeks to investigate this hypothesis. We designed two physically different human avatars that could follow the gaze of users via eye-tracking technology. In a preliminary experiment, 20 participants assessed the baseline appeal of the two avatars and confirmed that the avatars differed in this respect. In the main experiment, we compared how 19 participants rated the two avatars in terms of pleasantness, trustworthiness and closeness when the avatars were following their gaze versus when the avatar generated gaze movements autonomously. Although the same avatar as in the preliminary experiment was rated more favorably, the pleasantness attributed to the two avatars increased when they followed the gaze of the participants. This outcome provides evidence that gaze following fosters liking independently of the baseline appeal of the individual.

Smooth head tracking for virtual reality applications

In this work, we propose a new head-tracking solution for human-machine real-time interaction with virtual 3D environments. This solution leverages RGBD data to compute virtual camera pose according to the movements of the user's head. The process starts with the extraction of a set of facial features from the images delivered by the sensor. Such features are matched against their respective counterparts in a reference image for the computation of the current head pose. Afterwards, a prediction approach is used to guess the most likely next head move (final pose). Pythagorean Hodograph interpolation is then adapted to determine the path and local frames taken between the two poses. The result is a smooth head trajectory that serves as an input to set the camera in virtual scenes according to the user's gaze. The resulting motion model has the advantage of being: continuous in time, it adapts to any frame rate of rendering; it is ergonomic, as it frees the user from wearing tracking markers; it is smooth and free from rendering jerks; and it is also torsion and curvature minimizing as it produces a path with minimum bending energy.

Prediction of individual learning curves across information visualizations

Confident usage of information visualizations is thought to be influenced by cognitive aspects as well as amount of exposure and training. To support the development of individual competency in visualization processing, it is important to ascertain if we can track users' progress or difficulties they might have while working with a given visualization. In this paper, we extend previous work on predicting in real time a user's learning curve--a mathematical model that can represent a user's skill acquisition ability--when working with a visualization. First, we investigate whether results we previously obtained in predicting users' learning curves during visualization processing generalize to a different visualization. Second, we study to what extent we can make predictions on a user's learning curve without information on the visualization being used. Our models leverage various data sources, including a user's gaze behavior, pupil dilation, and cognitive abilities. We show that these models outperform a baseline that leverages knowledge on user task performance so far. Our best performing model achieves good accuracies in predicting users' learning curves even after observing users' performance on a few tasks only. These results represent an important step toward understanding how to support users in learning a new visualization.