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

CHS：中：虚拟现实模拟器疾病的预测、早期检测和缓解

基本信息

批准号：
1901423
负责人：
Evan Rosenberg
金额：
$ 110万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-15 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901423&HistoricalAwards=false
关键词：
CHS Medium Prediction Early Detection

项目摘要

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.

虚拟现实是一个快速发展的领域，在教育、培训、康复、医疗保健、社会通信和娱乐等领域都有许多新兴的应用。然而，虚拟现实应用的有效性及其公众采用率目前受到限制，因为许多用户在使用期间或之后会出现身体不适，并伴有晕动病的症状特征。这个问题被称为“模拟器病”或“晕屏病”，是沉浸式技术的用户、开发者和利益相关者面临的最大可用性挑战之一。该项目提供了一种新颖的、基于经验的研究方法来研究、预测、检测并最终减轻模拟器病，这可以大大提高当前和未来虚拟现实应用的主观用户体验和有效性。此外，先前的研究表明，晕动病对女性的影响更大。该项目旨在促进对这些差异的理解，并制定自适应策略，以减轻个人对模拟器的不适，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病，这将促进对运动运动学与虚拟现实系统用户通常经历的不良症状之间关系的基本理解。具体活动包括以下内容：(1)根据个人的运动特征开发预测患模拟器病可能性的模型；(2)引入实时方法，在用户感到不适之前早期发现疾病；(3)识别与模拟器病相关的特定问题虚拟现实刺激；(4)制定适应性缓解战略，以降低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上的创新，包括研究眼睛凝视稳定性以预测或早期检测模拟器晕动病。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，该技术可以根据个人预测的疾病水平和当前的实时状态自动调整，这两项都是通过定量运动运动学测量的。该项目还将创建一个大规模的运动运动学数据集，该数据集将为未来的研究公开提供。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。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Asymmetric Lateral Field-of-View Restriction to Mitigate Cybersickness During Virtual Turns

DOI：
10.1109/vr51125.2022.00028
发表时间：
2022-03
期刊：
2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
影响因子：
0
作者：
Fei Wu;Evan Suma Rosenberg
通讯作者：
Fei Wu;Evan Suma Rosenberg

Using quantitative data on postural activity to develop methods to predict and prevent cybersickness

使用姿势活动的定量数据来开发预测和预防晕机症的方法

DOI：
10.3389/frvir.2022.1001080
发表时间：
2022
期刊：
Frontiers in Virtual Reality
影响因子：
0
作者：
Bailey, George S.;Arruda, Danilo G.;Stoffregen, Thomas A.
通讯作者：
Stoffregen, Thomas A.

Redirected Tilting: Eliciting Postural Changes with a Rotational Self-Motion Illusion

重定向倾斜：通过旋转自我运动错觉引发姿势变化

DOI：
10.1109/vrw52623.2021.00040
发表时间：
2021
期刊：
IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops
影响因子：
0
作者：
Nie, Tongyu;Suma Rosenberg, Evan
通讯作者：
Suma Rosenberg, Evan

Like a Rolling Stone: Effects of Space Deformation During Linear Acceleration on Slope Perception and Cybersickness