Virtual reality (VR) simulations have been adopted to provide controllable environments for running augmented reality (AR) experiments in diverse scenarios. However, insufficient research has explored the impact of AR applications on users, especially their attention patterns, and whether VR simulations accurately replicate
these effects. In this work, we propose to analyze user attention patterns via eye tracking during XR usage. To represent applications that provide both helpful guidance and irrelevant information, we built a Sudoku Helper app that includes visual hints and potential distractions during the puzzle-solving period. We conducted two
user studies with 19 different users each in AR and VR, in which we collected eye tracking data, conducted gaze-based analysis, and trained machine learning (ML) models to predict user attentional states and attention control ability. Our results show that the AR app had a statistically significant impact on enhancing attention by
increasing the fixated proportion of time, while the VR app reduced fixated time and made the users less focused. Results indicate that there is a discrepancy between VR simulations and the AR experience. Our ML models achieve 99.3% and 96.3% accuracy in predicting user attention control ability in AR and VR, respectively. A noticeable performance drop when transferring models trained on one medium to the other further highlights the gap between the AR experience and the VR simulation of it.
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Predictive Caching for AR/VR Experiences in a Household Scenario
Augmented/virtual reality (AR/VR) technologies can be deployed in a household environment for applications such as checking the weather or traffic reports, watching a summary of news, or attending classes. Since AR/VR applications are highly delay sensitive, delivering these types of reports in maximum quality could be very challenging. In this paper, we consider that users go through a series of AR/VR experience units that can be delivered at different experience quality levels. In order to maximize the quality of the experience while minimizing the cost of delivering it, we aim to predict the users’ behavior in the home and the experiences they are interested in at specific moments in time. We describe a deep learning based technique to predict the users’ requests from AR/VR devices and optimize the local caching of experience units. We evaluate the performance of the proposed technique on two real-world datasets and compare our results with other baselines. Our results show that predicting users’ requests can improve the quality of experience and decrease the cost of delivery.
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- Award ID(s):
- 1800961
- PAR ID:
- 10159366
- Date Published:
- Journal Name:
- 2020 International Conference on Computing, Networking and Communications (ICNC)
- Page Range / eLocation ID:
- 591 to 595
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICNC47757.2020.9049692
