Gaze Point Research Articles

An audiovisual cognitive optimization strategy guided by salient object ranking for intelligent visual prothesis systems

Objective.Visual prostheses are effective tools for restoring vision, yet real-world complexities pose ongoing challenges. The progress in AI has led to the emergence of the concept of intelligent visual prosthetics with auditory support, leveraging deep learning to create practical artificial vision perception beyond merely restoring natural sight for the blind.Approach.This study introduces an object-based attention mechanism that simulates human gaze points when observing the external world to descriptions of physical regions. By transforming this mechanism into a ranking problem of salient entity regions, we introduce prior visual attention cues to build a new salient object ranking (SaOR) dataset, and propose a SaOR network aimed at providing depth perception for prosthetic vision. Furthermore, we propose a SaOR-guided image description method to align with human observation patterns, toward providing additional visual information by auditory feedback. Finally, the integration of the two aforementioned algorithms constitutes an audiovisual cognitive optimization strategy for prosthetic vision.Main results.Through conducting psychophysical experiments based on scene description tasks under simulated prosthetic vision, we verify that the SaOR method improves the subjects' performance in terms of object identification and understanding the correlation among objects. Additionally, the cognitive optimization strategy incorporating image description further enhances their prosthetic visual cognition.Significance.This offers valuable technical insights for designing next-generation intelligent visual prostheses and establishes a theoretical groundwork for developing their visual information processing strategies. Code will be made publicly available.

ViTGaze: gaze following with interaction features in vision transformers

Gaze following aims to interpret human-scene interactions by predicting the person’s focal point of gaze. Prevailing approaches often adopt a two-stage framework, whereby multi-modality information is extracted in the initial stage for gaze target prediction. Consequently, the efficacy of these methods highly depends on the precision of the previous modality extraction. Others use a single-modality approach with complex decoders, increasing network computational load. Inspired by the remarkable success of pre-trained plain vision transformers (ViTs), we introduce a novel single-modality gaze following framework called ViTGaze. In contrast to previous methods, it creates a novel gaze following framework based mainly on powerful encoders (relative decoder parameters less than 1%). Our principal insight is that the inter-token interactions within self-attention can be transferred to interactions between humans and scenes. Leveraging this presumption, we formulate a framework consisting of a 4D interaction encoder and a 2D spatial guidance module to extract human-scene interaction information from self-attention maps. Furthermore, our investigation reveals that ViT with self-supervised pre-training has an enhanced ability to extract correlation information. Many experiments have been conducted to demonstrate the performance of the proposed method. Our method achieves state-of-the-art performance among all single-modality methods (3.4% improvement in the area under curve score, 5.1% improvement in the average precision) and very comparable performance against multi-modality methods with 59% fewer parameters.

Towards Adapting CLIP for Gaze Object Prediction

This paper aims to investigate the problem of gaze object prediction in single images. We propose an application-friendly network based on CLIP for gaze object prediction. To avoid domain bias, we utilize a shallow feature adapter that transfers pre-trained features to target-oriented ones. Secondly, we introduce a pooling attention block to exploit the joint representation of multimodal elements, reducing gaze point deviation. Additionally, we introduce a loss that measures the prediction quality by comparing the distribution difference between the model's predictions heatmaps and the ground truth. Extensive experiments demonstrate the superior performance of our model compared to previous models. We will provide the method code at: https://github.com/fadaishaitaiyang/CCLIP.git.

A Study on Differences in Educational Method to Periodic Inspection Work of Nuclear Power Plants

Construction work and regular inspection work at nuclear power plants involve many special tasks, unlike general on-site work. In addition, the opportunity to transfer knowledge from skilled workers to unskilled workers is limited due to the inability to easily enter the plant and various security and radiation exposure issues. Therefore, in this study, we considered the application of virtual reality (VR) as a method to increase opportunities to learn anytime and anywhere and to transfer knowledge more effectively. In addition, as an interactive learning method to improve comprehension, we devised a system that uses hand tracking and eye tracking to allow participants to experience movements and postures that are closer to the real work in a virtual space. For hand-based work, three actions, “pinch”, “grab”, and “hold”, were reproduced depending on the sizes of the parts and tools, and visual confirmation work was reproduced by the movement of the gaze point of the eyes, faithfully reproducing the special actions of the inspection work. We confirmed that a hybrid learning process that appropriately combines the developed active learning method, using experiential VR, with conventional passive learning methods, using paper and video, can improve the comprehension and retention of special work at nuclear power plants.

MRGazer: decoding eye gaze points from functional magnetic resonance imaging in individual space

Objective. Eye-tracking research has proven valuable in understanding numerous cognitive functions. Recently, Freyet alprovided an exciting deep learning method for learning eye movements from functional magnetic resonance imaging (fMRI) data. It employed the multi-step co-registration of fMRI into the group template to obtain eyeball signal, and thus required additional templates and was time consuming. To resolve this issue, in this paper, we propose a framework named MRGazer for predicting eye gaze points from fMRI in individual space.Approach. The MRGazer consists of an eyeball extraction module and a residual network-based eye gaze prediction module. Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol, and achieves end-to-end eye gaze regression.Main results. The proposed method achieved superior performance in eye fixation regression (Euclidean error, EE = 2.04°) than the co-registration-based method (EE = 2.89°), and delivered objective results within a shorter time (∼0.02 s volume-1) than prior method (∼0.3 s volume-1).Significance. The MRGazer is an efficient, simple, and accurate deep learning framework for predicting eye movement from fMRI data, and can be employed during fMRI scans in psychological and cognitive research. The code is available athttps://github.com/ustc-bmec/MRGazer.

Gaze Scanning on Mid-Block Sidewalks by Pedestrians With Homonymous Hemianopia With or Without Spatial Neglect.

The purpose of this study was to investigate gaze-scanning by pedestrians with homonymous hemianopia (HH) when walking on mid-block sidewalks. Pedestrians with right homonymous hemianopia (RHH), and left homonymous hemianopia (LHH) without and with left spatial neglect (LHSN) walked on city streets wearing a gaze-tracking system. Gaze points were obtained by combining head movement and eye-in-head movement. Mixed-effects regression models were used to compare horizontal gaze scan magnitudes and rates between the side of the hemi-field loss (BlindSide) and the seeing side (SeeingSide), among the three subject groups, and between mid-block walking and street crossing segments. A total of 7021 gaze scans were obtained from 341minutes of mid-block walking videos by 19 participants (6 with LHH, 7 with RHH, and 6 with LHSN). The average gaze magnitude and scanning rate in mid-block segments were significantly higher towards the BlindSide than the SeeingSide in LHH (magnitude larger by 1.9° (degrees), P = 0.006; scan rate higher by 4.2 scans/minute, P < 0.001) and RHH subjects (magnitude larger by 3.3°, P < 0.001; scan rate higher by 3.2 scans/minute, P = 0.002), but they were not significantly different in LHSN subjects. The scanning rate, in terms of scans/minute (mean, 95% confidence interval [CI]) was significantly lower in LHSN subjects (mean = 6.9, 95% CI = 5.6-8.7) than LHH (mean = 10.2, 95% CI = 8.0-13.1; P = 0.03) and RHH (mean = 11.1, 95% CI = 9.0-13.7; P = 0.007) subjects. Compared to street-crossings, the scan rate during the mid-block segments was lower by 3.5 scans/minute (P < 0.001) and the gaze magnitude was smaller by 3.8° (P < 0.001) over the 3 groups. Evidence of compensatory scanning suggests a proactive, top-down mechanism driving gaze in HH. The presence of spatial neglect (SN) appeared to negatively impact the top-down process.

Scene‐content‐sensitive real‐time adaptive foveated rendering

AbstractIn recent years, techniques for accelerating rendering by exploiting the limitations of the human visual system have become increasingly prevalent. The foveated rendering method significantly reduces the computational requirements during rendering by reducing image quality in peripheral regions. In this paper, we propose a scene‐content‐sensitive real‐time adaptive foveated rendering method. First, we pre‐render the three‐dimensional (3D) scene at a low resolution. Then, we utilize the low‐resolution pre‐rendered image as input to extract edge, local contrast, and color features. Subsequently, we generate a screen‐space region division map based on the gaze point position. Next, we calculate the visual importance of each 16 × 16 pixel tile based on edge, local contrast, color, and screen‐space region. We then map the visual importance to the shading rate to generate a shading rate control map for the current frame. Finally, we complete the rendering of the current frame based on variable rate shading technology. Experimental results demonstrate that our method effectively enhances the visual quality of images near the foveal region while generating high quality foveal region images. Furthermore, our method can significantly improve performance compared to per‐pixel shading method and existing scene‐content‐based foveated rendering methods.

3D-Oculography: New Method of Determination of Human Gaze Point Position in Space

INTRODUCTION: Oculography — a method of recording movement of eyeballs of a human or animal by analyzing changes in electrical potentials recorded by two electrodes fixed on skin near the eye socket is used to solve various problems. Among them are the determination of pronounced nystagmus, of characteristic changes of oculomotor reactions in different conditions of observing video images. Of interest is the determination of the position of gaze point (GP) and of the area of increased attention in three-dimensional space. This information is associated with the cognitive system of the observer and is of interest not only to physiologists, but also to specialists of related fields. . AIM: To experimentally prove the effectiveness of the developed method of 3D-oculography providing determination of the GP position in space. MATERIALS AND METHODS: The possibility of restoring GP position based on the analysis of recorded oculograms even in the presence of additive noise induced by the external electromagnetic fields, is proven by the numeric modeling method. Direct experimental studies were conducted with recording oculograms of a human observer on a previously verified equipment. A ZB-2 oculograph was used, the data obtained were verified by the method of average values. RESULTS: The obtained modeling results permitted to determine the maximal values of dispersion of additive noise and amplitude shifts of signals, which make possible satisfactory restoration of the coordinates of the GP and parameters of its movement trajectory. The qualitative correspondence of the experimental results to the results of numerical modeling was proven. The correspondence between trajectories of the observed object moving in space and trajectories of GP movement synthesized from counts of recorded oculograms was confirmed. CONCLUSION: Multichannel record of oculographic signals permits restoring parameters of the GP trajectory. Creating systems for recording these signals requires minimization of the noise level. An increase in dispersion of the noise component of the signal leads to most significant errors in calculating the coordinates of trajectory points.

Emotional Design and Validation Study of Human–Landscape Visual Interaction

The formal beauty of “objects” is the main focus of modern rural landscapes, ignoring human interaction with the environment and the emotional reflection in this behavioral process. It is unable to satisfy the emotional needs of younger people who aspire to a high-quality life in the rural environment. The research idea of this paper is ‘first assessment—then design—then validation’. First, A 5-point Likert scale was used to investigate differences in contemporary young people’s emotional perceptions of the four rural natural landscapes in terms of instinct, behavior, and reflection. Then, using architectural design methods, a visual attraction element (viewing platform) was added by selecting samples that varied in all three dimensions (visual richness, behavioral attraction, and depth of thought). After that, a desktop eye tracker was used to record the eyeball characteristics of participants viewing the current images of natural landscapes and images of modified natural landscapes (pupil diameter, fixation duration, gaze point, etc.), and these data were combined with the subjective psychological perception scale score to determine whether or not the subjects’ positive emotions are evoked by the modified natural environment. The findings indicate that placing visually attractive elements between people and the natural world can cause subjects to feel good, think deeply, and feel more a part of the surroundings. Furthermore, we confirmed that subjects’ emotions can be evoked by 2D natural environment pictures and that the length of time subjects gaze at a picture is unaffected by the size of any individual element.

TSPM-Net: A probabilistic spatio-temporal approach for scanpath prediction

Predicting the path followed by the viewer’s eyes when observing an image (a scanpath) is a challenging problem, particularly due to the inter- and intra-observer variability and the spatio-temporal dependencies of the visual attention process. Most existing approaches have focused on progressively optimizing the prediction of a gaze point given the previous ones. In this work we propose instead a probabilistic approach, which we call tSPM-Net. We build our method to account for observers’ variability by resorting to Bayesian deep learning and a probabilistic approach. Besides, we optimize our model to jointly consider both spatial and temporal dimensions of scanpaths using a novel spatio-temporal loss function based on a combination of Kullback–Leibler divergence and dynamic time warping. Our tSPM-Net yields results that outperform those of current state-of-the-art approaches, and are closer to the human baseline, suggesting that our model is able to generate scanpaths whose behavior closely resembles those of the real ones.

Head motion-corrected eye gaze tracking with the da Vinci surgical system.

To facilitate the integration of point of gaze (POG) as an input modality for robot-assisted surgery, we introduce a robust head movement compensation gaze tracking system for the da Vinci Surgical System. Previous surgical eye gaze trackers require multiple recalibrations and suffer from accuracy loss when users move from the calibrated position. We investigate whether eye corner detection can reduce gaze estimation error in a robotic surgery context. A polynomial regressor is first used to estimate POG after an 8-point calibration, and then, using another regressor, the POG error from head movement is estimated from the shift in 2D eye corner location. Eye corners are computed by first detecting regions of interest using the You Only Look Once (YOLO) object detector trained on 1600 annotated eye images (open dataset included). Contours are then extracted from the bounding boxes and a derivative-based curvature detector refines the eye corner. Through a user study (n = 24), our corner-contingent head compensation algorithm showed an error reduction in degrees of visual angle of 1.20 (p = 0.037) for the left eye and 1.26 (p = 0.079) for the right compared to the previous gold-standard POG error correction method. In addition, the eye corner pipeline showed a root-mean-squared error of 3.57 (SD = 1.92) pixels in detecting eye corners over 201 annotated frames. We introduce an effective method of using eye corners to correct for eye gaze estimation, enabling the practical acquisition of POG in robotic surgery.

On current limitations of online eye-tracking to study the visual processing of source code

Context:Eye-tracking is an increasingly popular instrument to study how programmers process and comprehend source code. While most studies are conducted in controlled environments with lab-grade hardware, it would be desirable to simplify and scale participation in experiments for users sitting remotely, leveraging home equipment. Objective:This study investigates the possibility of performing eye-tracking studies remotely using open-source algorithms and consumer-grade webcams. It establishes the technology’s current limitations and evaluates the quality of the data collected by it. We conclude by recommending ways forward to address the shortcomings and make remote code-reading studies in support of eye-tracking feasible in the future. Method:We gathered eye-gaze data remotely from 40 participants performing a code reading experiment on a purpose-built web application. The utilized eye-tracker worked client-side and used ridge regression to generate x- and y-coordinates in real-time predicting the participants’ on-screen gaze points without the need to collect and save video footage. We processed and analysed the collected data according to common practices for isolating eye-movement events and deriving metrics used in software engineering eye-tracking studies. In response to the lack of an algorithm explicitly developed for detecting oculomotor fixation events in low-frequency webcam data, we also introduced a dispersion threshold algorithm for that purpose. The quality of the collected data was subsequently assessed to determine the adequacy and validity of the methodology for eye-tracking. Results:The collected data was found to be of varying quality despite extensive calibration and graphical user guidance. We present our results highlighting both the negative and positive observations from which the community hopefully can learn. Both accuracy and precision were low and ultimately deemed insufficient for drawing valid conclusions in a high-precision empirical study. We nonetheless contribute to identifying critical limitations to be addressed in future research. Apart from the overall challenge of vastly diverse equipment, setup, and configuration, we found two main problems with the current webcam eye-tracking technology. The first was the absence of a validated algorithm to isolate fixations in low-frequency data, compromising the assurance of the accuracy of the data derived from it. The second problem was the lack of algorithmic support for head movements when predicting gaze location. Unsupervised participants do not always keep their heads still, even if instructed to do so. Consequently, we frequently observed spatial shifts that corrupted many collected datasets. Three encouraging observations resulted from the study. Even when shifted, gaze points were consistently dispersed in patterns resembling both the shape and size of the stimuli without extreme deviations. We could also distinguish recognizable reading patterns. Linearity was significantly different when participants were reading source code compared to natural text, and we could detect the expected left-to-right and top-to-bottom reading directions for participants reading natural text snippets. Conclusion:The accuracy and precision levels were not sufficient for a word-by-word analysis of code reading but could be adequate for a broader, coarse-grained precision study. Additionally we identified two main issues compromising the collected data validity and contributed a fixation detection algorithm to approach one of these issues. With suitable solutions to the identified issues, remote eye-tracking studies with webcams on code reading could eventually be feasible.

Investigation of the influences of instructors and different media on learning attention with a wearable eye‐tracking system in the physical classrooms

AbstractBackgroundExamining student attention in physical classrooms is crucial, but it faces challenges due to the lack of accurate monitoring. Constraints posed by device limitations and the design of educational materials impede the integration of eye‐tracking technology in these settings.ObjectivesThis study aims to (1) develop a wearable eye‐tracking system specifically designed to monitor students' eye movements and gaze points on the projector screen within a physical classroom setting; (2) explore the impact of instructor gestures (by compare live instruction by an instructor and video‐recorded instruction) on student attention and examine the effectiveness of directing students' attention from text to image through instructor intervention.MethodsAn innovative wearable eye‐tracking system was developed to monitor learners' eye movements within the physical classroom. Twenty‐five students participated in the experiment, which included two approaches: classroom lectured by the instructor and by a video presentation.Results and ConclusionsThe results indicate that participants exhibit a stronger inclination to allocate additional time to text content than image content when receiving instruction through video presentations with a laser pointer in the physical classroom. This tendency can be attributed to the participants' requirement for longer reading and comprehension time in the absence of an instructor. Additionally, the instructor's gestures and body movements significantly impacted participants' fixation on text slides compared to the image slides. The heatmap analyses support these findings and further indicate that participants focus on the instructor's face rather than other body parts.TakeawaysThe wearable eye‐tracking technology developed in this study holds promise for future educational research, offering further exploration and analysis opportunities.

Investigation of user-product interaction by determining the focal points of visual interest in different types of kitchen furniture: An eye-tracking study

The furniture industry faces the challenge of designing furniture that attracts greater visual attention from customers, leading to increased sales and positive impacts on the industry, businesses, economy, and the environment. In pursuit of this objective, the imperative lies in the development of user-centric designs that amplify the interaction between users and products within the furniture sector. This research aims to identify the focal points of interest in kitchen furniture through the utilization of eye-tracking technology. By analyzing the gaze points of 60 participants observing 10 different kitchen furniture models introduced in the showroom, valuable insights can be provided to designers. These insights, in turn, empower designers to fabricate products that effectively capture heightened user attention. The investigation involves the assessment of average gaze counts and the generation of visual interest heatmaps. The discernments derived from the research underscore that areas at eye level garner a more pronounced level of attention. Furthermore, the study observes that factors such as material diversity, accessory utilization, and the integration of lighting elements exert a positive influence on visual interest. The results, within the context of neurodesign, offer valuable guidance to the furniture sector. These results can support decision-making in new product design, manufacturing, and marketing, enabling the industry to produce attention-grabbing furniture.

Eye Tracking Technology

Measurement of eye movement is an activity done with the help of eye tracking technology. In this activity, the movement of pupils is a respond towards the stimuli. It gives information as per the gaze point of the individual person. The biometric tools are used for analysing .[10].ET is being involved with most of the fields which includes HCI, Designs, Medicals, Psychology and Buisness Marketing.[6].This paper gives the explaination and importance of ET in the life of advancement and its history.It starts in the late 19th Century.ET is done by filming of the eye according to its behaviour.This Research Paper includes different types of fields in an explaination manner.It includes the process of how ET helps in Research based on previous evaluations.Eye is generally act as a tool of brain system.ET devices respond to the movement of stimuli

Eye-tracking-based analysis of pharmacists’ thought processes in the dispensing work: research related to the efficiency in dispensing based on right-brain thinking

BackgroundPharmacists should be aware of their thought processes in dispensing work, including differences in the dispensing complexities owing to different drug positions in the left, center, and right areas. Dispensing errors associated with “same-name drugs (a pair of drugs with the same name but a different ingredient quantity)” are prevalent and often negatively affect patients. In this study, using five pairs of comparative models, the gaze movements of pharmacists in dispensing work were analyzed using an eye-tracking method to elucidate their thought processes.MethodsWe prepared verification slides and displayed them on a prescription monitor and three drug rack monitors. The dispensing information (drug name, drug usage, location display, and total amount) was displayed on a prescription monitor. A total of 180 drugs including five target drugs were displayed on the three drug rack monitors. Total gaze points in the prescription area, those in the drug rack area, total vertical movements between the two areas, and time required to dispense drugs were measured as the four classifications Gaze 1, Gaze 2, Passage, and Time, respectively. First, we defined the two types of location displays as “numeral combination” and “color/symbol combination.” Next, we defined two pairs of models A1-A2 (numerals) and B1-B2 (color/symbol) to compare differences between the left and right areas. Moreover, three pairs of models C1-C2 (left), D1-D2 (center), and E1-E2 (right) were established to compare differences between “numeral combination” and “color/symbol combination.”ResultsSignificant differences in the complexities of dispensing work were observed in Gaze 2, Passage, and Time between the models A1-A2 (A1<A2), in Gaze 2 between the models B1-B2 (B1>B2), and in Gaze 2 and Time between the models C1-C2, D1-D2, and E1-E2 (C1>C2, D1>D2, and E1>E2, respectively).ConclusionsUsing the current dispensing rules, pharmacists are not good at dispensing drugs located in the right area. An effective measure for reducing the dispensing complexity is to introduce visual information in the prescription content; the utilization of the right brain facilitates reducing the complexity in the right dispensing area.

A study of spatial cognition in the rural heritage based on VR 3D eye-tracking experiments

Traditional Chinese villages act as the essential carriers of China’s rural heritage. Such villages are considered to retain living practices of intangible heritage through know-how, artistic and folk customs, as well as a well-preserved architectural appearance. However, their conservation is at stake due to degradation, abandonment, and urbanization. Spatial cognition is one way of bringing people to the matter of what is important to conserve (attributes) and why they are important (values). This is done by analysing how people perceive and value rural heritage. Traditional methods of gathering spatial cognition data, such as laborious fieldwork or subjective qualitative analyses, often fall short of providing a holistic representation of real-world experiences. This article presents an innovative method for analysing cognitive features in traditional village spaces using an immersive virtual reality equipped with an eye-tracking system. We selected Cheng and Jitou, two traditional Chinese villages, as our case studies. In two virtual reality environments, we captured accurate 3D cognitive data, including participants’ locomotion, gaze point, and sightline. The results indicate that the accessibility of village roads affects the distribution of people’s locomotion, which in turn affects the distribution of people’s areas of interest; the changes in the rhythm of the sightline scale reflect the rhythm of spatial changes in the villages. By broadening the understanding of spatial cognition in traditional Chinese villages related to people’s values, this paper sheds light on an alternative approach to assess the cultural significance of such villages for higher conservation and sustainability.

Eye-Hand Typing: Eye Gaze Assisted Finger Typing via Bayesian Processes in AR.

Nowadays, AR HMDs are widely used in scenarios such as intelligent manufacturing and digital factories. In a factory environment, fast and accurate text input is crucial for operators' efficiency and task completion quality. However, the traditional AR keyboard may not meet this requirement, and the noisy environment is unsuitable for voice input. In this article, we introduce Eye-Hand Typing, an intelligent AR keyboard. We leverage the speed advantage of eye gaze and use a Bayesian process based on the information of gaze points to infer users' text input intentions. We improve the underlying keyboard algorithm without changing user input habits, thereby improving factory users' text input speed and accuracy. In real-time applications, when the user's gaze point is on the keyboard, the Bayesian process can predict the most likely characters, vocabulary, or commands that the user will input based on the position and duration of the gaze point and input history. The system can enlarge and highlight recommended text input options based on the predicted results, thereby improving user input efficiency. A user study showed that compared with the current HoloLens 2 system keyboard, Eye-Hand Typing could reduce input error rates by 28.31 % and improve text input speed by 14.5%. It also outperformed a gaze-only technique, being 43.05% more accurate and 39.55% faster. And it was no significant compromise in eye fatigue. Users also showed positive preferences.

Augmenting collaborative interaction with shared visualization of eye movement and gesture in VR

AbstractVirtual Reality (VR)‐enabled multi‐user collaboration has been gradually applied in academic research and industrial applications, but it still has key problems. First, it is often difficult for users to select or manipulate objects in complex three‐dimesnional spaces, which greatly affects their operational efficiency. Second, supporting natural communication cues is crucial for cooperation in VR, especially in collaborative tasks, where ambiguous verbal communication cannot effectively assign partners the task of selecting or manipulating objects. To address the above issues, in this paper, we propose a new interaction method, Eye‐Gesture Combination Interaction in VR, to enhance the execution of collaborative tasks by sharing the visualization of eye movement and gesture data among partners. We conducted user experiments and showed that using dots to represent eye gaze and virtual hands to represent gestures can help users complete tasks faster than other visualization methods. Finally, we developed a VR multi‐user collaborative assembly system. The results of the user study show that sharing gaze points and gestures among users can significantly improve the productivity of collaborating users. Our work can effectively improve the efficiency of multi‐user collaborative systems in VR and provide new design guidelines for collaborative systems in VR.

Optimising virtual object position for efficient eye-gaze interaction in Hololens2

ABSTRACT Our study explored eye-tracking technology in the Hololens2 HMD. We assessed the effectiveness of eye-gaze interactions in a 3D environment, particularly in text entry applications. Existing recommendations for the placement and size of virtual objects are often followed without empirical validation. Therefore, we evaluated text entry target selection rates within the manufacturer’s specified optimal gaze interaction zone. We measured the spatial accuracy and precision of eye gaze data and optimised target positions for enhanced text entry performance. By establishing confidence ellipses covering 95% of gaze points per target, we derived an Area of Interest (AOI) recalibration function. Applying a receiver operating characteristic-based method, we quantified the recalibrated tracker’s performance at various AOIs. Our results indicate an optimal recalibrated AOI radius is 0.036 m, and the ideal object-plane distance from the eye-plane is 2.25 m. This recalibrated specification allows to efficiently interact with Hololens2 via eye movement, achieving target selection rates exceeding 90%.