HCC: Small: Making Virtual Reality Safe
- Institution
- University of Texas at San Antonio, TX
- Principal investigator
- John P Quarles
- Funding
- $600K
- Duration
- 2024–2026
Moonbase score
Still being scored
Abstract
Although consumer-level virtual reality head-mounted displays have become affordable enough for a broad user base to purchase, there are still serious concerns about safety. Head-mounted displays block out the surrounding real world, which can hide obstacles, such as tables, pets, walls, or other potential collision hazards. Current approaches to avoiding collisions depend on the user to define play area boundaries; this process is subject to user error and thus can lead to injury. Moreover, current approaches are ineffective for games that require fast motions, as the systems may not react in time to prevent injury. To address these problems, the investigators will create camera-based methods for detecting potential collisions in real time and evaluate feedback techniques to reduce the incidence of injury when using virtual reality headsets. This approach has the potential to make the use of virtual reality much safer in real-world environments. The objective of this project is to create and evaluate reconstruction, segmentation, and motion-prediction techniques to inform obstacle avoidance feedback and reduce the incidence of injury to people using virtual reality head mounted displays. Providing feedback tailored to the specific locations of the user's body that are in proximity to real obstacles will, ideally, reduce user collisions with real-world objects compared to prior approaches. Specifically, the team will 1) determine the best approaches to real obstacle detection and segmentation, 2) investigate the efficacy of full body motion prediction approaches, 3) ascertain the optimal modality and locations of real obstacle alerts to maximize presence while minimizing collisions, and 4) evaluate the longitudinal impact of real obstacle alert systems in virtual reality systems. Ultimately, this research will result in human motion datasets that can be used for future motion prediction in virtual reality research, as well as open-source plugins that will make current virtual reality experiences safer by reducing injuries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- John P QuarlesPI
- Kevin Desai
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