Gaze Analysis in PD Games: The influence of aggression and relationship between before and after decision making on gaze behavior

This paper targets small- to medium-sized-group face-to-face conversations where each person wears a dual-view camera, consisting of inward- and outward-looking cameras, and presents an almost fully automatic but accurate ofline gaze analysis framework that does not require users to perform any calibration steps. Our collective first-person vision framework, where captured audio–visual signals are gathered and processed in a centralized system, jointly undertakes the fundamental functions required for group gaze analysis, including speaker detection, face tracking, and gaze tracking. Of particular note is our self-calibration of gaze trackers by exploiting a general conversation rule, namely that listeners are likely to look at the speaker. From the rough conversational prior knowledge, our system visualizes fine-grained participants’ gaze behavior as a gazee-centered heat map, which quantitatively reveals what parts of the gazee's body the participant looked at and for how long while the gazer was speaking or listening. An experiment using conversations amounting to a total of 140 min, each lasting an average of 8.7 min and engaged in by 37 participants in groups of three to six, achieves a mean absolute error of 2.8 $^\circ$ in gaze tracking. A statistical test reveals neither a group size effect nor a conversation type effect. Our method achieves F-scores of over 0.89 and 0.87 in gazee and eye contact recognition, respectively, in comparison with human annotation.

Gaze behaviour: A window into distinct cognitive processes revealed by the Tower of London test

This paper presents a real-time system platform for profiling and analyzing the gaze behavior based on visual contents. The proposed system captures the gaze information from multiple users and provides the ability to measure the degree of visual content perception by the users through statistical analysis. Visual content representation scheme is presented for capturing and annotating the gaze behavior effectively. Information correlation property among multiple image frames is defined for providing the ability to analyze the pattern of perception of user based on complex visual contents. In order to monitor the real time gaze behavior of the users, the monitoring rules are incorporated in to the representation template. The number of users and the duration of observation time may significantly increase the profile data size. To alleviate the problem, an adaptive data compression techniques is incorporated. The capabilities and functionalities of the proposed system are verified for multiple users with different gaze behavior on a sequence of commercial image data.

This paper extends the affective computing research field by introducing first-person vision to automatic conversation analysis. We target medium-sized-party face-to-face conversations where each person wears inward-looking and outward-looking cameras. We demonstrate that the fundamental techniques required for group gaze analysis, i.e. speaker detection, face tracking, and gaze estimation, can be accurately and effectively performed via self-training in a unified framework by gathering captured audio-visual signals to a centralized system and using a general conversation rule, i.e. listeners look mainly at the speaker. We visualize the characteristics of participants' gaze behavior as a gazee-centered heat map, which quantitatively reveals what parts of the gazee's body and for how long the participant looked at it while the gazer speaks or listens. An experiment involving two groups of six-person conversations demonstrates the potential of the proposed framework.

Aim: To non-invasively detect hypoglycemia in individuals with type 1 diabetes (T1D) based on gaze behavior while driving.Methods: Controlled hypoglycemia was induced in 19 individuals (12 males, age 32 ± 7.1 yrs) with T1D (HbA1c 7.1 ± 0.6% [54 ± 6 mmol/mol]) using an adapted hypoglycemic clamp protocol. Gaze and blood glucose (BG) data were gathered while driving in a simulator during three 18 min sessions: session 1 (BG 90-144 mg/dL), session 2 (BG declining from 72 to 45 mg/dL), and session 3 (BG 36-45 mg/dL). A gradient-boosting machine learning (ML) model was built for hypoglycemia (BG < 70 mg/dL) detection based on gaze behavior.Results: Mean venous BG was 105.4 ± 11.4 mg/dL during session 1, declined from 61.4 ± 6.1 mg/dL to 47.2 ± 8.5 mg/dL during session 2, and was 42.7 ± 4.1 mg/dL during session 3, respectively. Gaze analysis provided 29,968 data samples (1,577.5 ± 52 per subject, 10,041 euglycemia, 19,927 hypoglycemia). Overall, ML achieved an area under the receiver-operating-characteristics curve of 0.83 ± 0.09 for hypoglycemia detection with leave-one-subject-out cross-validation.Conclusion: ML-based gaze analysis shows high accuracy in non-invasive hypoglycemia detection while driving. Our approach offers promising potential in various settings where cameras are available.View largeDownload slideView largeDownload slide DisclosureV. Lehmann: None. E. Fleisch: Research Support; Self; CSS Health Insurance, Switzerland, Stock/Shareholder; Self; Pathmate-Technologies AG, Switzerland. F. Wortmann: None. C. Stettler: None. M. Maritsch: None. T. Zueger: None. A. Marxer: None. C. Bérubé: None. M. Kraus: None. C. Albrecht: None. S. Feuerriegel: None. T. Kowatsch: Advisory Panel; Self; Pathmate Technologies AG, Switzerland and Germany, Stock/Shareholder; Self; Pathmate Technologies AG, Switzerland and Germany.FundingSwiss National Science Foundation (SNF CRSII5_183569); Insel Group; Diabetes Center Berne

In virtual reality (VR) systems, users’ gaze information has gained importance in recent years. It can be applied to many aspects, including VR content design, eye-movement based interaction, gaze-contingent rendering, etc. In this context, it becomes increasingly important to understand users’ gaze behaviors in virtual reality and to predict users’ gaze positions. This paper presents research in gaze behavior analysis and gaze position prediction in virtual reality. Specifically, this paper focuses on static virtual scenes and dynamic virtual scenes under free-viewing conditions. Users’ gaze data in virtual scenes are collected and statistical analysis is performed on the recorded data. The analysis reveals that users’ gaze positions are correlated with their head rotation velocities and the salient regions of the content. In dynamic scenes, users’ gaze positions also have strong correlations with the positions of dynamic objects. A data-driven eye-head coordination model is proposed for real-time gaze prediction in static scenes and a CNN-based model is derived for predicting gaze positions in dynamic scenes.

The present study aims to explore the status of filled pauses as pragmatic markers by taking into account their accompanying visual and gestural behavior. This aspect has not yet been widely explored, and the current study breaks new ground by demonstrating that the analysis of gaze and gesture can shed substantial light on the pragmatic functions of filled pauses and other pausing phenomena. Filled pauses (FPs) serve several pragmatic functions in speech, mainly planning but also turn-holding and emphasis, and their use is also highly determined by register and setting. This research explores the different pragmatic functions of FPs by analyzing their distribution in two different communication settings (conversation vs presentation setting), combining a quantitative and a qualitative methodology, following Kosmala &amp; Crible’s (2021) study on the same data. Particular attention was paid to the co-occurring gestural activity of uh/ums and gaze behavior. Analyses show that the pragmatic functions of FPs are also embodied in kinetic activities which differ according to the setting: more pragmatic and referential ones were found during FPs in conversation than in the presentation setting, as well as more eye-contact, which reflects their potential communicative role during interactional sequences.

Updating and complementing a previous review on eye-tracking technology and the dynamics of natural gaze behavior in sports, this short review focuses on the progress concerning researched sports tasks, applied methods of gaze data collection and analysis as well as derived gaze measures for the time interval of 2016–2022. To that end, a systematic review according to the PRISMA guidelines was conducted, searching Web of Science, PubMed Central, SPORTDiscus, and ScienceDirect for the keywords: eye tracking, gaze behavio*r, eye movement, and visual search. Thirty-one studies were identified for the review. On the one hand, a generally increased research interest and a wider area of researched sports with a particular increase in official’s gaze behavior were diagnosed. On the other hand, a general lack of progress concerning sample sizes, amounts of trials, employed eye-tracking technology and gaze analysis procedures must be acknowledged. Nevertheless, first attempts to automated gaze-cue-allocations (GCA) in mobile eye-tracking studies were seen, potentially enhancing objectivity, and alleviating the burden of manual workload inherently associated with conventional gaze analyses. Reinforcing the claims of the previous review, this review concludes by describing four distinct technological approaches to automating GCA, some of which are specifically suited to tackle the validity and generalizability issues associated with the current limitations of mobile eye-tracking studies on natural gaze behavior in sports.

Gaze analysis and prediction in interactive virtual environments, such as games, is a challenging topic since the 3D perspective and variations of the viewpoint as well as the current task introduce many variables that affect the distribution of gaze. In this article, we present a novel pipeline to study eye-tracking data acquired from interactive 3D applications. The result of the pipeline is an importance map which scores the amount of gaze spent on each object. This importance map is then used as a heuristic to predict a user's visual attention according to the object properties present at runtime. The novelty of this approach is that the analysis is performed in object space and the importance map is defined in the feature space of high-level properties. High-level properties are used to encode task relevance and other attributes, such as eccentricity, which may have an impact on gaze behavior. The pipeline has been tested with an exemplary study on a first-person shooter game. In particular, a protocol is presented describing the data acquisition procedure, the learning of different importance maps from the data, and finally an evaluation of the performance of the derived gaze predictors. A metric measuring the degree of correlation between attention predicted by the importance map and the actual gaze yielded clearly positive results. The correlation becomes particularly strong when the player is attentive to an in-game task.

The advanced technology of eye-tracking enables us to analyse healthcare professionals’ (HCPs) gaze behaviours. Gaze analysis has great potential to capture HCPs’ non-technical skills, especially situational awareness (SA).1 The SA framework has three levels. Level 1 involves perceiving an event, level 2 understanding what is being perceived importantly and level 3 being able to make predictions. How to analyse HCPs’ utterances and gaze in an integrative manner may provide insights into higher-order cognitive skills such as level 3 SA. This study aims to establish a method to describe HCPs’ gaze and utterances in emergency care interactions, focusing on a leader’s gaze at team members’ faces and bodies when making requests. One simulated training session (about 16 min) was analysed, applying a multimodal corpus approach. The recording took place in the resuscitation area at Yokohama City University Medical Center. The team comprised a senior consultant as a team leader (Leader) with an eye-tracker, Tobii Pro Glasses 2, another two doctors (a senior doctor (SD) and a junior doctor (JD)), a foundation doctor and two nurses, and a simulated patient (male, …

We present an eye tracking system developed to study the driver's gaze behavior while driving a car. Two main challenges distinguish this project from similar works with analogous purposes: (1) the use of a cheap portable remote eye tracker, normally employed for indoor applications in controlled light conditions; and (2) the use of an ordinary webcam to shoot the road in front of the car. The purpose is to generate a video where the driver's fixations are graphically and dynamically superimposed on the road shot by webcam (taking into account the different viewpoints of driver and camera). The carried-out experiments confirmed that the implemented system, while perfectible, fulfills the requirements initially set.

This study investigates how the walking speed of a pedestrian ahead and aisle width influence conflict and gaze behavior during overtaking selection. The experiment employed a virtual environment with a head-mounted display (MetaQuest Pro/Meta) and an omnidirectional treadmill (Virtualizer Elite 2/Cyberith GmbH). The experiment involved 13 university students, who walked through three types of spaces in sequence: “Training Space,” “Reference Speed Measuring Space,” and “Analysis Space.” The analysis space consisted of 9 conditions, defined by three speed ratios (the ratio of the pedestrian ahead's walking speed to the participant's reference speed: 0.7, 0.8, and 0.9) and three aisle widths (2.5, 3.0, and 3.5 m). During walking, torso rotation angles and Yaw angles of gaze were measured to calculate three analytical indices: “Overtaking Rate,” “Overtaking Selection Time,” and “Gazing Dispersion.”The analysis of the overtaking rate suggested that action selections were likely made based on the relative magnitudes of the following burden (attributed to the speed ratio) and the overtaking burden (attributed to the aisle width), which were anticipated during walking. For overtaking selection time, the results indicated that the influence of the following burden varied depending on aisle width, with narrower aisle widths likely causing greater conflict in overtaking selections. Furthermore, for gazing dispersion, the findings suggested that the equilibrium between the following burden and the overtaking burden could influence the distribution of gaze.These findings represent a novel contribution by demonstrating the potential to quantify conflict due to action selection through gaze analysis. Based on these results, seamless measurement of anticipated burdens during action selection may become feasible.

Examining the effect of locomotion techniques on virtual prototype assessment: Gaze analysis using a Head-Mounted Display

Objective This study aimed to analyze gaze behavior when viewing real-world driving videos in a virtual reality (VR) environment. Methods A total of 22 driving instructors and 36 older drivers, all with regular driver’s licenses, participated in this study. Participants watched 360° real-world driving videos using a head-mounted display equipped with eye-tracking functionality (FOVE 0). Areas of interest (AOIs), such as traffic signals and side mirrors, were designated within the videos. Gaze behavior was evaluated using indicators such as time to first fixation, dwell time (DT), and revisit count (RC). Results In many scenarios, older drivers reached the AOIs significantly later, had shorter DT, and lower RC than those of driving instructors. These differences were particularly pronounced during right and left turns and lane changes, suggesting that older drivers may have insufficient recognition of surrounding risks. In addition, older drivers exhibited delays in visual attention, indicating a lack of attention to the surrounding environment. Conclusions Gaze analysis in a VR environment is a valuable method for safely evaluating the gaze behavior of older drivers. This study revealed important differences in gaze behavior between driving instructors and older drivers. These findings have practical implications for improving the safety of older drivers. By understanding their unique gaze behavior, targeted interventions can be developed to improve the safety of older drivers.

The current paper discusses how individuals will process social media content within the metaverse world. Also, the current paper will propose an exploratory study that is designed to provide preliminary evidence regarding how individuals cognitively and emotionally process social media posts embedded in a metaverse platform where they experience the simulation of becoming their desired or positive &#x201C;future self&#x201D;. From data obtained from the gaze tracking and social media engagement metrics that measure users&#x0027; simulated attentional and engagement behaviors, the author will examine to what extent the different temporal distances of virtual or simulated self (present vs near- vs far-future self) and the actual self (lowly vs highly conscientious self) interactively influences the durations of attention (duration of viewing the posts) to and the engagement (i.e., clicking &#x201C;like&#x201D; button for the posts) in different social media posts (e.g., news feeds, positive vs negative dog pictures in native adverts, a health marketing post) seen in the virtual computer screen within the virtual room. In general, it is expected that the father the temporal distance, the longer the duration of the attention to the social media posts. This effect can be moderated by the actual self-views. That is, for participants with negative self-views, the farther the simulated temporal distance, the shorter the duration of attention to the social media posts relevant for the positive self within the metaverse environment. The opposite results are expected to be observed from individuals with positive self-views: the farther the simulated temporal distance, the longer the duration of attention to the social media posts seen within the non-real or metaverse world. The analysis and the plan for the presentation at 2022 IEEE VR workshop is provided in this paper.