Detection and prediction of cybersickness in virtual and mixed reality environments using wearables

使用可穿戴设备检测和预测虚拟和混合现实环境中的晕眩症

• 批准号: 576732-2022
• 负责人: Falk, TiagoTH
• 金额: $ 3.64万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752603
• 关键词: Detection; prediction; cybersickness; virtual; mixed

Virtual and mixed reality (VR/MR) systems have burgeoned over the last couple of years with applications in healthcare, gaming, telepresence, and skills training, to name a few. Within the skills training sector, for example, police/law enforcement training is an important application domain which has seen increased adoption worldwide. In Canada, Public Safety and the Royal Canadian Mounted Police (RCMP) have invested millions of dollars to set up state-of-the-art VR/MR facilities to train the next generation of law enforcement agents. VR allows for different scenes, conditions, and maneuvers to be tested in one single physical location, thus not only reducing training costs, but better equipping trainees to handle unknowns. While the potential is there, existing VR/MR systems are known to induce motion sickness - known as cybersickness - on a large proportion of the trainees, especially females. As such, cybersickness detection and prevention methods are drastically needed in order to provide a more inclusive training environment. This project aims to solve this problem via the use of multimodal signal processing of wearable device signals. When coupled with the Sensor Hub system developed by the industry partner, Thales Canada, the project proposes to quantify cybersickness under four different locomotion conditions and develop a cybersickness index that can be used in trainee evaluations. In particular, locomotion in VR via steering with controllers, teleporting, and physical movement will be compared against physical movement in mixed reality. Cybersickness indices will be developed based on signals measured in real-time from an in-house developed instrumented headset, a smartshirt, and a smartwatch. Anthropometric measures will also be explored in order to remove any potential biases that may be caused by biological sex. The tools developed herein will enable new applications for the industry partner's Sensor Hub, allowing them to reach a new clientele relying on emerging VR/MR applications, as well as provide invaluable insights and tools for Public Safety Canada and the RCMP to make their training and evaluation protocols more inclusive and equitable.

在过去的几年中，虚拟和混合现实（VR/MR）系统在医疗保健，游戏，关注和技能培训中的申请中迅速发展。例如，在技能培训部门中，警察/执法培训是一个重要的应用领域，在全球范围内采用的采用率都在增加。在加拿大，公共安全和加拿大皇家骑警（RCMP）投资了数百万美元，以建立最先进的VR/MR设施来培训下一代执法人员。 VR允许在一个单个物理位置进行测试不同的场景，条件和操纵，因此不仅降低了培训成本，而且还可以更好地装备受训者来处理未知数。尽管潜力存在，但已知现有的VR/MR系统在很大一部分受训者（尤其是女性）上引起了运动疾病（称为Cyber​​sickness）。因此，为了提供更具包容性的培训环境，需要急需使用Cyber​​sickness检测和预防方法。该项目旨在通过使用可穿戴设备信号的多模式信号处理来解决此问题。当加拿大Thales Canada与行业合作伙伴开发的传感器集线器系统相结合时，该项目建议在四种不同的机车条件下量化Cyber​​sickness，并开发可用于受训者评估的Cyber​​sickness指数。特别是，通过转向控制器，传送和身体运动的转向，将与混合现实中的身体运动进行比较。 Cyber​​sickness索引将根据内部开发的仪器耳机，智能衫和智能手表实时测量的信号开发。还将探索人体测量指标，以消除可能由生物学性别引起的任何潜在偏见。本文开发的工具将为行业合作伙伴的传感器枢纽提供新的应用程序，使他们能够依靠新兴的VR/MR应用程序达到新客户，并为加拿大公共安全和加拿大皇家骑警提供宝贵的见解和工具，以使他们的培训和评估协议更具包容性和公平性。