Collaborative Research: HCC: Medium: HCI in Motion -- Using EEG, Eye Tracking, and Body Sensing for Attention-Aware Mobile Mixed Reality

移动、无线、虚拟和增强现实耳机，如Meta Quest 2和微软HoloLens-2，正越来越广泛地应用于视频游戏以外的许多应用，如培训、建筑和医疗。然而，戴着这种头戴式护目镜走路会让一些人感到恶心或分心，在某些情况下甚至会导致受伤。这种影响类似于边走边发短信，但可能更糟，因为一个人的整个外围都可能充满了分散注意力的媒体元素。虽然之前的研究已经调查了用户站着不动或坐着时的这些问题，但目前还不清楚问题是如何出现的，以及如何在用户运动时预防这些问题。具体来说，这个项目将调查虚拟现实和增强现实耳机如何以及为什么会影响注意力和生病的感觉。首先，这项工作将记录用户佩戴虚拟现实和增强现实耳机走路时的心率、脑电波和视线转向等数据。其次，该项目将开发使用虚拟现实和增强现实耳机走路时减少疾病和分心的方法。这项工作将提高移动虚拟现实和增强现实耳机的安全性，如今几乎所有大型科技公司都在大力投资这些产品，希望它们能成为智能手机的伴侣或替代品。该项目将被引入到德克萨斯大学圣安东尼奥分校和加州大学圣巴巴拉分校的课程和研究指导项目中，以促进本科生和研究生的研究训练。考虑到大学和研究团队都有支持许多未被充分代表的少数民族学生的历史，预计该项目的教育价值将很高，特别是在招募和指导女性和未被充分代表的少数民族学生方面。移动沉浸式界面(例如，移动虚拟现实（VR) /增强现实（AR））越来越普遍，可能会影响用户的认知能力和态势感知，可能导致身体伤害（例如，任务性能受损，在VR中被物理障碍绊倒，在AR中看到广告时穿过不安全的街道）。人机交互的场景已经从相当标准化的固定办公室配置扩展到更多样化的移动和沉浸式设置，包括主动身体运动（移动和定位计算，AR，移动VR）和模拟第一人称视角变化和运动体验（沉浸式计算）。更糟糕的是，与更标准化的平台（如台式机和笔记本电脑ui，平板电脑和智能手机界面）相比，用户之间的个体差异对移动AR/VR中上下文驱动的围绕焦点使用场景的可用性影响更大。例如，众所周知，VR中的晕动病（即晕动病）会造成严重程度差异很大的症状，这取决于个人用户。一个严重的后果是，交互设计师很难提供吸引人的通用体验，让各种各样的用户都能普遍使用。尽管沉浸式技术及其缺陷越来越普遍，但“动态计算”的确切认知和生理机制还没有得到很好的理解。这项工作旨在填补这一知识空白。具体目标是：1)评估用户行走时与移动AR/VR交互的认知效果；2)提供自动化工具，有效降低移动AR/VR的认知需求；3)提高移动AR/VR的安全性和可用性。基于初步数据，中心假设是通过多模态传感与机器学习方法相结合，移动AR/VR应用可以学习用户行为的特征，并提供实时适应，从而减少用户错误，增加易用性，提高任务性能，减少物理危害的影响。这项工作将提高移动AR和移动VR的安全性，几乎所有大型科技公司今天都在大力投资，作为智能手机平台的后续范例。通过将研究成果纳入德克萨斯大学圣安东尼奥分校和加州大学圣巴巴拉分校的本科和研究生课程，以及为本科生和研究生提供研究培训和指导机会，将产生教育影响。课程包括机器学习、深度学习视觉计算、人机交互和移动应用程序编程。由于我们的项目将具有高度社会影响力的主题与尖端机器学习和人机交互研究以及已证明成功的指导策略相结合，因此项目的教育影响将很高，特别是在招聘和指导女性和代表性不足的少数民族学生方面。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Mobile, wireless, headsets for virtual and augmented reality, such as the Meta Quest 2 and Microsoft HoloLens-2, are becoming more widely used in many applications beyond video games, such as training, construction, and medicine. However, wearing these head-worn goggles while walking can make some people feel sick or distracted, which has even led to injury in some cases. This effect is similar to texting while walking, but potentially worse because a person's entire periphery can be filled with distracting media elements. While previous research has investigated these issues when users are standing still or seated, it is unclear how problems unfold and how they can be prevented while users are in motion. Specifically, this project will investigate how and why virtual and augmented reality headsets affect attention and feelings of sickness. First, this work will record data, such as heart rate, brain waves, and the direction users are turning their eyes to, while they are wearing virtual and augmented reality headsets and walking. Secondly, this project will develop ways to reduce sickness and distraction while walking with virtual and augmented reality headsets. This work will improve the safety of mobile virtual and augmented reality headsets, products that virtually all big technology companies today heavily invest in as possible companions or replacements to smartphones. This project will be introduced in courses and research mentorship projects at The University of Texas at San Antonio and the University of California at Santa Barbara, to advance research training of both undergraduate and graduate students. Considering that both universities and research teams have a history of supporting many underrepresented minority students, it is expected that the educational value of this project will be high, especially in terms of recruiting and mentoring women and underrepresented minority students.There is an increasing prevalence of mobile, immersive interfaces (e.g., mobile Virtual Reality(VR) / Augmented Reality (AR)) that may affect users' cognitive capacities and situational awareness, potentially leading to physical harm (e.g., impaired task performance, tripping over physical obstacles in VR, unsafe street crossings while seeing advertisements in AR). The landscape of human-computer interaction has expanded from fairly well standardized stationary office configurations to more varied mobile and immersive settings involving active body movements (mobile and situated computing, AR, mobile VR) and simulated first-person perspective changes and motion experiences (immersive computing). To make matters worse, compared to more standardized platforms such as desktop and laptop UIs, tablet and smartphone interfaces, individual differences among users have a much bigger usability impact in context-driven surround-focus usage scenarios found in mobile AR/VR. For example, motion sickness (i.e., cybersickness) in VR is known to inflict symptoms of widely varying severity, depending on the individual user. One serious consequence is that interaction designers have difficulties providing engaging general experiences that are universally usable by a wide variety of users. Despite the increasing prevalence of immersive technologies and their pitfalls, the precise cognitive and physiological mechanisms at play when 'computing in motion' are not well understood. This work is aimed at filling this knowledge gap. The specific objectives are: 1) to assess the cognitive effects of interacting with mobile AR/VR while users are walking, 2) to provide automated tools to effectively reduce the cognitive demand of mobile AR/VR, and 3) to make mobile AR/VR safer and more usable. Based on preliminary data, the central hypothesis is that through multi-modal sensing combined with machine learning approaches, mobile AR/VR applications can learn the characteristics of user behavior and provide real time adaptations that will reduce user error, increase ease of use, improve task performance, and reduce the impact of physical hazards. This work will improve the safety of mobile AR and mobile VR, paradigms that virtually all big technology companies today heavily invest in as a possible follow-up paradigm to the smartphone platform. Educational impact will occur through incorporation of research outcomes into undergraduate and graduate courses offered at The University of Texas at San Antonio and the University of California at Santa Barbara, and research training and mentorship opportunities for both undergraduate and graduate students. The courses include Machine Learning, Deep Learning for Visual Computing, Human-Computer Interaction, and Mobile Application Programming. Because our project integrates a topic of high social impact with cutting edge machine learning and human-computer interaction research along with proven successful mentorship strategies, the educational impacts of the project will be high, especially in terms of recruiting and mentoring women and underrepresented minority students.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.