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1. GazeXR: A generally eye-tracking system enabling invariable gaze data in virtual environment

   https://doi.org/10.1007/978-3-030-77599-5_4  

   Lenart, C. (January 2021, Virtual, augmented and mixed reality)

   J. Y. C., Chen (Ed.)

   Controlling and standardizing experiments is imperative for quantitative research methods. With the increase in the availability and quantity of low-cost eye-tracking devices, gaze data are considered as an important user input for quantitative analysis in many social science research areas, especially incorporating with virtual reality (VR) and augmented reality (AR) technologies. This poses new challenges in providing a default interface for gaze data in a common method. This paper propose GazeXR, which focuses on designing a general eye-tracking system interfacing two eye-tracking devices and creating a hardware independent virtual environment. We apply GazeXR to the in-class teaching experience analysis use case using external eye-tracking hardware to collect the gaze data for the gaze track analysis. 

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2. User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications

   https://doi.org/10.3390/virtualworlds1010004  

   Asish, Sarker Monojit; Kulshreshth, Arun K.; Borst, Christoph W. (December 2022, Virtual Worlds)

   Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work. 

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3. Eye Tracking in Virtual Reality: a Broad Review of Applications and Challenges

   https://doi.org/10.1007/s10055-022-00738-z  

   Adhanom, Isayas Berhe; MacNeilage, Paul; Folmer, Eelke (January 2023, Virtual Reality)

   Abstract Eye tracking is becoming increasingly available in head-mounted virtual reality displays with various headsets with integrated eye trackers already commercially available. The applications of eye tracking in virtual reality are highly diversified and span multiple disciplines. As a result, the number of peer-reviewed publications that study eye tracking applications has surged in recent years. We performed a broad review to comprehensively search academic literature databases with the aim of assessing the extent of published research dealing with applications of eye tracking in virtual reality, and highlighting challenges, limitations and areas for future research. 

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4. i-FlatCam: A 253 FPS, 91.49 µJ/Frame Ultra-Compact Intelligent Lensless Camera for Real-Time and Efficient Eye Tracking in VR/AR

   https://doi.org/10.1109/VLSITechnologyandCir46769.2022.9830458  

   Zhao, Yang; Li, Ziyun; Fu, Yonggan; Zhang, Yongan; Li, Chaojian; Wan, Cheng; You, Haoran; Wu, Shang; Ouyang, Xu; Boominathan, Vivek; et al (June 2022, 2022 Symposium on VLSI Technology & Circuits Digest of Technical Papers)

   We present a first-of-its-kind ultra-compact intelligent camera system, dubbed i-FlatCam, including a lensless camera with a computational (Comp.) chip. It highlights (1) a predict-then-focus eye tracking pipeline for boosted efficiency without compromising the accuracy, (2) a unified compression scheme for single-chip processing and improved frame rate per second (FPS), and (3) dedicated intra-channel reuse design for depth-wise convolutional layers (DW-CONV) to increase utilization. i-FlatCam demonstrates the first eye tracking pipeline with a lensless camera and achieves 3.16 degrees of accuracy, 253 FPS, 91.49 µJ/Frame, and 6.7mm×8.9mm×1.2mm camera form factor, paving the way for next-generation Augmented Reality (AR) and Virtual Reality (VR) devices. 

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5. Exploring Eye Gaze Visualization Techniques for Identifying Distracted Students in Educational VR

   https://doi.org/10.1109/VR46266.2020.00009  

   Rahman, Yitoshee; Asish, Sarker M.; Fisher, Nicholas P.; Bruce, Ethan C.; Kulshreshth, Arun K.; Borst, Christoph W. (March 2020, 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR))

   Virtual Reality (VR) headsets with embedded eye trackers are appearing as consumer devices (e.g. HTC Vive Eye, FOVE). These devices could be used in VR-based education (e.g., a virtual lab, a virtual field trip) in which a live teacher guides a group of students. The eye tracking could enable better insights into students’ activities and behavior patterns. For real-time insight, a teacher’s VR environment can display student eye gaze. These visualizations would help identify students who are confused/distracted, and the teacher could better guide them to focus on important objects. We present six gaze visualization techniques for a VR-embedded teacher’s view, and we present a user study to compare these techniques. The results suggest that a short particle trail representing eye trajectory is promising. In contrast, 3D heatmaps (an adaptation of traditional 2D heatmaps) for visualizing gaze over a short time span are problematic. 

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