HCC: Small: Making Virtual Reality Safe

尽管消费级虚拟现实头戴式显示器已经变得足够便宜，可以让广泛的用户群购买，但仍然存在严重的安全问题。头戴式显示器遮挡了周围的真实的世界，这可以隐藏障碍物，例如桌子、宠物、墙壁或其他潜在的碰撞危险。目前避免碰撞的方法依赖于用户定义游戏区域边界;该过程易受用户错误的影响，因此可能导致伤害。此外，目前的方法对于需要快速运动的游戏是无效的，因为系统可能无法及时做出反应以防止受伤。为了解决这些问题，研究人员将创建基于摄像头的方法，用于真实的检测潜在的碰撞，并评估反馈技术，以减少使用虚拟现实耳机时受伤的发生率。这种方法有可能使虚拟现实的使用更加安全，在现实世界的environments.The项目的目标是创建和评估重建，分割和运动预测技术，告知避障反馈和减少受伤的发生率使用虚拟现实头戴式显示器的人。与现有方法相比，提供针对接近真实的障碍物的用户身体的特定位置定制的反馈将理想地减少用户与真实世界对象的碰撞。具体来说，该团队将1）确定真实的障碍物检测和分割的最佳方法，2）调查全身运动预测方法的有效性，3）确定真实的障碍物警报的最佳模态和位置，以最大限度地提高存在性，同时最大限度地减少碰撞，以及4）评估虚拟现实系统中真实的障碍物警报系统的纵向影响。最终，这项研究将产生可用于虚拟现实研究中未来运动预测的人体运动数据集，以及通过减少伤害使当前虚拟现实体验更安全的开源插件。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.