A TRADE-OFF MODEL TO ALLEVIATE CYBERSICKNESS

我们概述了一个关于晕屏病的广泛研究项目。在第一阶段，我们将进行单个分数因子实验，以确定从omega平方分析中生成数据的可行性，并将其输入isperformance等权衡模型。第一阶段的实证研究将使用疾病的自我报告作为主要因变量，尽管也将获得相关（和更客观的因变量）。晕屏病的决定因素，包括设备配置、人类适应因素、暴露时间表和易感性的个体差异，将会有所不同和研究。第二阶段，如果获得批准，将继续这一计划，包括探索响应面（Williges, 1981），并最终产生一个软件数据库，供制造商、工程师、开发人员和VR系统的其他用户使用，以指定和实现低水平的晕屏。这个软件将是交互式的，这样数据库就可以随着VR的新功能进入市场而被修改，这些发现可以被纳入模型中。在第三阶段，我们计划为VR行业开发、营销和销售isperformance软件和数据库，以提供各种晕动症决定因素之间的权衡分析。拟议的商业应用：如果晕动病的发病率和严重程度是可预测的，并且如果能够开发出一种数据分析模型来权衡主要决定因素，那么虚拟现实在生物医学上用于复杂外科手术的任务排练、治疗用途（恐惧症、记忆丧失、闭合性头部创伤的康复）和生物医学人员的广泛培训将会更加安全和具有成本效益。

We outline a broad program of research into cybersickness. In Phase I we will conduct a single fractional factorial experiment to determine the feasibility of generating data from an omega squared analyses to be entered into a trade-off model such as Isoperformance. The Phase I empirical study will be conducted using self-report of sickness as the main dependent variable, although related (and more objective dependent variables) will also be obtained. The determinants of cybersickness, including equipment configurations, human adaptation factors, schedule of exposures, and individual differences in susceptibility will be varied and studied. Phase II, if awarded, will continue this plan including those exploring response surfaces (Williges, 1981) and ultimately produce a software database that may be used by manufacturers, engineers, developers and other users of VR systems, in order to specify and achieve low levels of cybersickness. This - software will be deliberately interactive so that the database can be modified as new features in VR come on the market and these findings can be incorporated into the model. In Phase III we plan to develop, market and sell the Isoperformance software and database to the VR industry to provide tradeoff analyses among the various cybersickness determinants. PROPOSED COMMERCIAL APPLICATION: Biomedical uses of virtual reality for mission rehearsal of complicated surgical procedures, therapeutic uses (phobias, memory loss, rehabilitation from closed head trauma), and broad training of biomedical personnel will be much safer and cost effective if cybersickness incidence and severity were predictable and if a data analytic model to trade off the major determiners could be developed.