- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
- Page Range or eLocation-ID:
- 868 to 877
- Sponsoring Org:
- National Science Foundation
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VR displays (HMDs) with embedded eye trackers could enable better teacher-guided VR applications since eye tracking could provide insights into student’s activities and behavior patterns. We present several techniques to visualize eye-gaze data of the students to help a teacher gauge student attention level. A teacher could then better guide students to focus on the object of interest in the VR environment if their attention drifts and they get distracted or confused.
We present and evaluate methods to redirect desktop inputs such as eye gaze and mouse pointing to a VR-embedded avatar. We use these methods to build a novel interface that allows a desktop user to give presentations in remote VR meetings such as conferences or classrooms. Recent work on such VR meetings suggests a substantial number of users continue to use desktop interfaces due to ergonomic or technical factors. Our approach enables desk-top and immersed users to better share virtual worlds, by allowing desktop-based users to have more engaging or present "cross-reality" avatars. The described redirection methods consider mouse pointing and drawing for a presentation, eye-tracked gaze towards audience members, hand tracking for gesturing, and associated avatar motions such as head and torso movement. A study compared different levels of desktop avatar control and headset-based control. Study results suggest that users consider the enhanced desktop avatar to be human-like and lively and draw more attention than a conventionally animated desktop avatar, implying that our interface and methods could be useful for future cross-reality remote learning tools.
Deep Learning on Eye Gaze Data to Classify Student Distraction Level in an Educational VR EnvironmentEducational VR may increase engagement and retention compared to traditional learning, for some topics or students. However, a student could still get distracted and disengaged due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting distraction. For example, we previously considered gaze visualizations to help teachers understand student attention to better identify or guide distracted students. However, it is not practical for a teacher to monitor a large numbers of student indicators while teaching. To help filter students based on distraction level, we consider a deep learning approach to detect distraction from gaze data. The key aspects are: (1) we created a labeled eye gaze dataset (3.4M data points) from an educational VR environment, (2) we propose an automatic system to gauge a student's distraction level from gaze data, and (3) we apply and compare three deep neural classifiers for this purpose. A proposed CNN-LSTM classifier achieved an accuracy of 89.8\% for classifying distraction, per educational activity section, into one of three levels.
Underwater robots, including Remote Operating Vehicles (ROV) and Autonomous Underwater Vehicles (AUV), are currently used to support underwater missions that are either impossible or too risky to be performed by manned systems. In recent years the academia and robotic industry have paved paths for tackling technical challenges for ROV/AUV operations. The level of intelligence of ROV/AUV has increased dramatically because of the recent advances in low-power-consumption embedded computing devices and machine intelligence (e.g., AI). Nonetheless, operating precisely underwater is still extremely challenging to minimize human intervention due to the inherent challenges and uncertainties associated with the underwater environments. Proximity operations, especially those requiring precise manipulation, are still carried out by ROV systems that are fully controlled by a human pilot. A workplace-ready and worker-friendly ROV interface that properly simplifies operator control and increases remote operation confidence is the central challenge for the wide adaptation of ROVs.
This paper examines the recent advances of virtual telepresence technologies as a solution for lowering the barriers to the human-in-the-loop ROV teleoperation. Virtual telepresence refers to Virtual Reality (VR) related technologies that help a user to feel that they were in a hazardous situation without being present at the actual location. We present amore »
Abstract Objective.Reorienting is central to how humans direct attention to different stimuli in their environment. Previous studies typically employ well-controlled paradigms with limited eye and head movements to study the neural and physiological processes underlying attention reorienting. Here, we aim to better understand the relationship between gaze and attention reorienting using a naturalistic virtual reality (VR)-based target detection paradigm. Approach.Subjects were navigated through a city and instructed to count the number of targets that appeared on the street. Subjects performed the task in a fixed condition with no head movement and in a free condition where head movements were allowed. Electroencephalography (EEG), gaze and pupil data were collected. To investigate how neural and physiological reorienting signals are distributed across different gaze events, we used hierarchical discriminant component analysis (HDCA) to identify EEG and pupil-based discriminating components. Mixed-effects general linear models (GLM) were used to determine the correlation between these discriminating components and the different gaze events time. HDCA was also used to combine EEG, pupil and dwell time signals to classify reorienting events. Main results.In both EEG and pupil, dwell time contributes most significantly to the reorienting signals. However, when dwell times were orthogonalized against other gaze events, the distributions of themore »