Evaluating the Scalability of Non-Preferred Hand Mode Switching in Augmented Reality

Jesse Smith, Isaac Wang, Winston Wei, Julia Woodward, J. Ruiz
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引用次数: 3

Abstract

Mode switching allows applications to support a wide range of operations (e.g. selection, manipulation, and navigation) using a limited input space. While the performance of different mode switching techniques has been extensively examined for pen- and touch-based interfaces, investigating mode switching in augmented reality (AR) is still relatively new. Prior work found that using non-preferred hand is an efficient mode switching technique in AR. However, it is unclear how the technique performs when increasing the number of modes, which is more indicative of real-world applications. Therefore, we examined the scalability of non-preferred hand mode switching in AR with two, four, six, and eight modes. We found that as the number of modes increase, performance plateaus after the four-mode condition. We also found that counting gestures have varying effects on mode switching performance in AR. Our findings suggest that modeling mode switching performance in AR is more complex than simply counting the number of available modes. Our work lays a foundation for understanding the costs associated with scaling interaction techniques in AR.
增强现实中非首选手模式切换的可扩展性评估
模式切换允许应用程序使用有限的输入空间支持广泛的操作(例如选择,操作和导航)。虽然不同模式切换技术的性能已经广泛地研究了基于笔和触摸的界面,但研究增强现实(AR)中的模式切换仍然是相对较新的。先前的研究发现,在AR中使用非首选手是一种有效的模式切换技术。然而,当增加模式数量时,该技术的表现如何尚不清楚,这更能说明现实世界的应用。因此,我们研究了AR中2、4、6和8种模式的非首选手模式切换的可扩展性。我们发现,随着模式数量的增加,在四模式条件下,性能趋于稳定。我们还发现计数手势对AR中的模式切换性能有不同的影响。我们的研究结果表明,在AR中建模模式切换性能比简单地计数可用模式的数量要复杂得多。我们的工作为理解AR中缩放交互技术的相关成本奠定了基础。
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