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

虚拟现实在全球拥有超过2800万的安装用户群，是一个快速发展的领域，在教育、培训、康复、医疗保健、社交通信和娱乐方面有着众多新兴应用。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：许多用户在其使用期间或之后经历身体不适，具有指示晕动病的症状特征。这个问题被称为“模拟器病”或“电脑病”，是沉浸式技术的用户，开发人员和利益相关者最重要的可用性挑战之一。该项目提供了一种新颖且基于经验的研究方法来研究、预测、检测并最终减轻模拟器疾病，这可以大大改善主观用户体验以及当前和未来虚拟现实应用程序的有效性。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，并制定适应性策略，以减轻个人层面的模拟器疾病，这最终可以增加全球潜在用户的总数，并消除目前存在的参与沉浸式技术的不公平障碍。该项目旨在通过系统的努力来解决模拟器疾病，这将促进对运动之间关系的基本理解。运动学和虚拟现实系统的用户通常经历的不良症状。具体活动包括：（1）开发模型，根据个人的运动特征预测经历模拟器疾病的可能性;（2）引入实时方法，在用户经历不适之前早期检测疾病发作;（3）识别与模拟器疾病相关的特定有问题的虚拟现实刺激;（4）制定适应性缓解战略，以减少不良症状的可能性和严重程度;（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，包括研究用于预测或早期检测模拟器疾病的眼睛凝视稳定性。此外，一个关键的创新是，从实证研究中收集的数据将用于开发自适应技术，这些技术可以根据个人的预测疾病水平和当前实时状态进行自动调整，两者都是通过定量运动学测量的。该项目还将创建一个大规模的运动学数据集，将公开提供给未来的研究。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.