Choosing adequate gestures for touchless interfaces is a challenging task that has a direct impact on human–computer interaction. Such gestures are commonly determined by the designer, ad-hoc, rule-based, or agreement-based methods. Previous approaches to assess agreement grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this article, we propose a generalized framework that inherently incorporates the gesture descriptors into the agreement analysis. In contrast to previous approaches, we represent gestures using binary description vectors and allow them to be partially similar. In this context, we introduce a new metric referred to as soft agreement rate ($\mathcal {SAR}$) to measure the level of agreement and provide a mathematical justification for this metric. Furthermore, we perform computational experiments to study the behavior of $\mathcal {SAR}$ and demonstrate that existing agreement metrics are a special case of our approach. Our method is evaluated and tested through a guessability study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by $\mathcal {SAR}$ is 2.64 times higher than the previous metrics. Finally, we show that our approach complements the existing agreement techniques by generating an artificial lexicon based on the most agreed properties.

中文翻译：

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使用手势描述分析的一致性研究

为非接触式界面选择合适的手势是一项具有挑战性的任务，对人机交互有直接影响。此类手势通常由设计者、临时、基于规则或基于协议的方法确定。先前评估一致性的方法将手势分组为等价类，并忽略了它们之间共享的整体属性。在本文中，我们提出了一个通用框架，该框架本质上将手势描述符合并到了一致性分析中。与之前的方法相比，我们使用二进制描述向量表示手势并允许它们部分相似。在这种情况下，我们引入了一个新的指标，称为软协议率（$\mathcal {SAR}$) 来衡量一致性水平并为此指标提供数学依据。此外，我们进行了计算实验来研究$\mathcal {SAR}$并证明现有的协议指标是我们方法的一个特例。我们的方法是通过与一组神经外科医生进行的可猜测性研究来评估和测试的。然而，我们的公式可以应用于任何其他用户启发研究。结果表明，通过获得的一致性水平$\mathcal {SAR}$是之前指标的 2.64 倍。最后，我们表明我们的方法通过基于最一致的属性生成人工词典来补充现有的协议技术。