Wei Yushi, Xu Kemu, Li Yue, Yu Lingyun, Liang Hai-Ning
IEEE Trans Vis Comput Graph. 2024 Nov;30(11):7107-7117. doi: 10.1109/TVCG.2024.3456166. Epub 2024 Oct 10.
Steering is a fundamental task in interactive Virtual Reality (VR) systems. Prior work has demonstrated that movement direction can significantly influence user behavior in the steering task, and different interactive environments (VEs) can lead to various behavioral patterns, such as tablets and PCs. However, its impact on VR environments remains unexplored. Given the widespread use of steering tasks in VEs, including menu adjustment and object manipulation, this work seeks to understand and model the directional effect with a focus on barehand interaction, which is typical in VEs. This paper presents the results of two studies. The first study was conducted to collect behavioral data with four categories: movement time, average movement speed, success rate, and reenter times. According to the results, we examined the effect of movement direction and built the SθModel. We then empirically evaluated the model through the data collected from the first study. The results proved that our proposed model achieved the best performance across all the metrics (r2 > 0.95), with more than 15% improvement over the original Steering Law in terms of prediction accuracy. Next, we further validated the SθModel by another study with the change of device and steering direction. Consistent with previous assessments, the model continues to exhibit optimal performance in both predicting movement time and speed. Finally, based on the results, we formulated design recommendations for steering tasks in VEs to enhance user experience and interaction efficiency.
在交互式虚拟现实(VR)系统中,操控是一项基本任务。先前的研究表明,运动方向会在操控任务中显著影响用户行为,并且不同的交互环境(VE)会导致各种行为模式,例如平板电脑和个人电脑。然而,其对VR环境的影响仍未得到探索。鉴于操控任务在VE中广泛应用,包括菜单调整和对象操纵,这项工作旨在理解并建立定向效应模型,重点关注裸手交互,这在VE中很典型。本文展示了两项研究的结果。第一项研究旨在收集四类行为数据:移动时间、平均移动速度、成功率和重新进入次数。根据结果,我们研究了运动方向的影响并建立了Sθ模型。然后,我们通过从第一项研究收集的数据对该模型进行了实证评估。结果证明,我们提出的模型在所有指标上都取得了最佳性能(r2>0.95),在预测准确性方面比原始的操控定律提高了超过15%。接下来,我们通过另一项关于设备和操控方向变化的研究进一步验证了Sθ模型。与之前的评估一致,该模型在预测移动时间和速度方面继续表现出最佳性能。最后,基于这些结果,我们为VE中的操控任务制定了设计建议,以提高用户体验和交互效率。