CHS: Medium: Prediction, Early Detection, and Mitigation of Virtual Reality Simulator Sickness

虚拟现实是一个快速发展的领域，拥有超过2800万的全球安装用户基础，在教育、培训、康复、医疗保健、社交通信和娱乐领域有着众多新兴应用。然而，虚拟现实应用的有效性及其公众采用率目前受到以下事实的限制：许多用户在使用期间或之后会感到身体不适，症状特征表明患有晕动病。这个问题被称为“模拟器病”或“计算机病”，是沉浸式技术的用户、开发人员和利益相关者面临的最重大的可用性挑战之一。该项目提供了一种新的、基于经验的研究方法来研究、预测、检测并最终缓解模拟器疾病，它可以显著改善用户的主观体验，并提高当前和未来虚拟现实应用的有效性。此外，先前的研究表明，晕动病对女性的影响不成比例。该项目旨在促进对这些差异的理解，并开发自适应策略来在个人层面上减轻模拟器疾病，这最终可以增加全球潜在用户的总体数量，并削弱目前存在的参与沉浸式技术的不公平障碍。该项目寻求通过系统的努力来解决模拟器疾病问题，这将促进对运动运动学和虚拟现实系统用户通常经历的不良症状之间的关系的基本理解。具体活动包括：(1)开发根据个人的运动特征预测经历模拟器疾病的可能性的模型；(2)引入在用户经历不适之前早期检测疾病发生的实时方法；(3)确定与模拟器疾病有关的特定有问题的虚拟现实刺激；(4)开发自适应缓解策略以降低不良症状的可能性和严重性；以及(5)对新开发技术的有效性和权衡进行严格的实验评估。该项目在与晕动病现象的基础研究相关的几个领域提供了方法创新，包括为预测或早期发现模拟器疾病而调查眼睛凝视稳定性。此外，一项关键的创新是，从经验研究中收集的数据将被用于开发自适应技术，根据个人预测的疾病水平和当前的实时状态自动调整，两者都通过定量运动运动学测量。该项目还将创建一个大规模运动运动学数据集，并将公开提供给未来的研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With a global installed user base of over 28 million people, virtual reality is a rapidly advancing field with numerous emerging applications in education, training, rehabilitation, healthcare, social communications, and entertainment. However, the effectiveness of virtual reality applications and their rate of public adoption is currently limited by the fact that many users experience physical discomfort during or after their use, with symptomatic characteristics indicative of motion sickness. This problem, known as "simulator sickness" or "cybersickness", is one of the most significant usability challenges for users, developers, and stakeholders of immersive technologies. This project offers a novel and empirically-grounded research methodology to study, predict, detect, and ultimately mitigate simulator sickness, which can substantially improve both the subjective user experience and the effectiveness of current and future virtual reality applications. Furthermore, prior research has shown that motion sickness disproportionately affects women. This project seeks to advance understanding of these differences and develop adaptive strategies for mitigating simulator sickness on an individual level, which can ultimately increase the overall number of potential users worldwide and erode the inequitable barriers that currently exist for engaging with immersive technologies.This project seeks to address simulator sickness through a systematic effort that will advance fundamental understanding of the relationship between motion kinematics and the adverse symptoms commonly experienced by users of virtual reality systems. Specific activities include the following: (1) development of models that predict the likelihood of experiencing simulator sickness based on an individual's motion characteristics; (2) introduction of real-time methods for early detection of sickness onset before the user experiences discomfort; (3) identification of specific problematic virtual reality stimuli that are associated with simulator sickness; (4) development of adaptive mitigation strategies to reduce the likelihood and severity of adverse symptoms; and (5) rigorous experimental evaluation of the effectiveness and tradeoffs of newly developed techniques. The project offers methodological innovation in several areas related to the fundamental study of motion sickness phenomena, including the investigation of eye gaze stability for the prediction or early detection of simulator sickness. Additionally, a key innovation is that the data collected from empirical studies will be utilized to develop adaptive techniques that adjust automatically based on the individual's predicted sickness levels and current real-time state, both measured through quantitative motion kinematics. The project will also result in the creation of a large-scale motion kinematics dataset that will be made publicly available for future research.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.