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Creators/Authors contains: "Kulshreshth, Arun K."

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  1. Free, publicly-accessible full text available October 9, 2024
  2. Free, publicly-accessible full text available August 5, 2024
  3. 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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  4. Educational VR may help students by being more engaging or improving retention compared to traditional learning methods. However, a student can get distracted in a VR environment due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting these distraction. We explore deep-learning-based approaches to detect distractions from gaze data. We designed an educational VR environment and trained three deep learning models (CNN, LSTM, and CNN-LSTM) to gauge a student’s distraction level from gaze data, using both supervised and unsupervised learning methods. Our results show that supervised learning provided better test accuracy compared to unsupervised learning methods. 
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  7. 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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