Pedagogical Agent Responsive to Eye Tracking in Educational VR

Khokhar, Adil; Yoshimura, Andrew; Borst, Christoph W.

doi:10.1109/vr.2019.8797896

Cited by 20 publications

(3 citation statements)

References 4 publications

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“…We employed an online SSR technologies to further show how their technique works as a hands-free interface in real-world virtual reality applications. The work shown in teaches students to use deep CNN-LSTM classifier to detect and eliminate distractions in a virtual reality (VR) classroom setting [13] utilizes generic characteristics taken from data collected by VR headset sensors (head-tracking, hand-tracking, as well as eye-tracking) such as angular velocity, positional velocity, pupil diameter, and eye openness. Their classifier outperforms two previous methods in terms of accuracy and feature generalizability, and they provide early findings showing an accuracy of 94.93%.…”

Section: Related Workmentioning

confidence: 99%

A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks

Manukonda

2024

IJCE

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One of the most anticipated features of 5G and subsequent networks is wireless virtual reality (VR), which promises to transform human interaction via its immersive experiences and game-changing capabilities. Wireless virtual reality systems, and VR games in particular, are notoriously slow due to rendering issues. But most academics don't care about data correlation or real-time rendering. Using mobile edge computing (MEC) and mmWave-enabled wireless networks, we provide an adaptive VR system that enables high-quality wireless VR. By using this architecture, VR rendering operations may be adaptively offloaded to MEC servers in real-time, resulting in even greater performance advantages via caching.The limited processing power of VR devices, the need for a high quality of experience (QoE), and the small latency in VR activities make it difficult to connect wireless VR consumers to high-quality VR content in real-time. To solve these problems, we provide a wireless VR network that is enabled by MEC. This network makes use of recurrent neural networks (RNNs) to provide real-time predictions about each user's field of vision (FoV). It is feasible to simultaneously move the rendering of virtual reality material to the memory of the MEC server. To improve the long-term VR users' quality of experience (QoE) while staying within the VR interaction latency limitation, we provide decoupling deep reinforcement learning algorithms that are both centrally and distributedly run, taking into consideration the connection between requests' fields of vision and their locations. When compared with rendering on VR headsets, our proposed MEC rendering techniques and DRL algorithms considerably improve VR users' long-term experience quality and reduce VR interaction latency, according to the simulation results.

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Section: Related Workmentioning

confidence: 99%

A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks

Manukonda

2024

IJCE

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“…With the successful and effective implementation of eyetracking in virtual reality into medical education and training, it makes sense to interpolate these findings into other fields of education. There is also the potential of implementing eyetracking technologies in virtual reality to engineering education (Khokhar et al, 2019). Kang et al (2020) describe the so-called Deepwater Horizon operation, where VR allows the assessment of the awareness of trainees and operators in a non-dangerous environment.…”

Section: Eye-tracking In Virtual Reality For Educationmentioning

confidence: 99%

Eye-Tracking in Immersive Virtual Reality for Education: A Review of the Current Progress and Applications

2022

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The concept of using eye-tracking in virtual reality for education has been researched in various fields over the past years. With this review, we aim to discuss the recent advancements and applications in this area, explain the technological aspects, highlight the advantages of this approach and inspire interest in the field. Eye-tracking has already been used in science for many decades and now has been substantially reinforced by the addition of virtual and augmented reality technologies. The first part of the review is a general overview of eye-tracking concepts, technical parts, and their applications. In the second part, the focus shifted toward the application of eye-tracking in virtual reality. The third part, first the description of the recently emerged concept of eye-tracking in virtual reality is given, followed by the current applications to education and studying, which has not been thoroughly described before. We describe the main findings, technological aspects, and advantages of this approach.

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“…arrows or lines, that direct the attention of the pupils towards critical objects that are currently discussed. In the educational environment of (Khokhar et al, 2019) the knowledge about inattentive students is provided to a pedagogical agent. Sakamoto et al (2020) tested pupil metrics for their eligibility to gain information about the comprehension of people.…”

Section: Virtual Classroomsmentioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

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Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.

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Pedagogical Agent Responsive to Eye Tracking in Educational VR

Cited by 20 publications

References 4 publications

A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks

A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks

Eye-Tracking in Immersive Virtual Reality for Education: A Review of the Current Progress and Applications

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

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