IEEE Trans Vis Comput Graph. 2022 May;28(5):2288-2298. doi: 10.1109/TVCG.2022.3150466. Epub 2022 Apr 11.
Developing effective strategies for redirected walking requires extensive evaluations across a variety of factors that influence performance. Because these large-scale experiments are often not practical with user studies, researchers have instead utilized simulations to systematically test different algorithm parameters, physical space configurations, and virtual walking paths. Although simulation offers an efficient way to evaluate redirected walking algorithms, it remains an open question whether this evaluation methodology is ecologically valid. In this paper, we investigate the interaction between locomotion behavior and redirection gains at a micro-level (across small path segments) and macro-level (across an entire experience). This examination involves analyzing data from real users and comparing algorithm performance metrics with a simulated user model. The results identify specific properties of user locomotion behavior that influence the application of redirected walking gains and resets. Overall, we found that the simulation provided a conservative estimate of the average performance with real users and observed that performance trends when comparing two redirected walking algorithms were preserved. In general, these results indicate that simulation is an empirically valid evaluation methodology for redirected walking algorithms.
开发有效的重定向行走策略需要对影响性能的各种因素进行广泛评估。由于这些大规模实验在用户研究中通常不切实际,因此研究人员转而利用模拟来系统地测试不同的算法参数、物理空间配置和虚拟行走路径。虽然模拟提供了一种评估重定向行走算法的有效方法,但这种评估方法是否具有生态有效性仍然是一个悬而未决的问题。在本文中,我们从微观层面(跨越小路径段)和宏观层面(跨越整个体验)研究了运动行为和重定向增益之间的相互作用。这项研究涉及分析真实用户的数据,并将算法性能指标与模拟用户模型进行比较。结果确定了影响重定向行走增益和重置应用的用户运动行为的具体特性。总的来说,我们发现模拟为真实用户的平均性能提供了保守的估计,并且观察到在比较两种重定向行走算法时性能趋势得到了保留。总的来说,这些结果表明,模拟是重定向行走算法的一种经验有效的评估方法。