Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
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DOI：
10.1145/1294211.1294238
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发表时间：
2007-10
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影响因子：
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通讯作者：
J. Wobbrock;Andrew D. Wilson;Yang Li
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文献类型：
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作者：
J. Wobbrock;Andrew D. Wilson;Yang Li
尽管移动、平板、大显示器和桌面计算机越来越多地提供在用户界面中使用笔、手指和棒手势的机会，但实现手势识别在很大程度上一直是模式匹配专家的特权，而不是用户界面原型的特权。尽管一些用户界面库和工具包提供了手势识别器，但此类基础设施在面向设计的环境(如Flash)、脚本环境(如JavaScript)或全新的非桌面原型环境中往往不可用。为了使新手程序员能够将手势合并到他们的UI原型中，我们提供了一个“$1识别器”，它简单、廉价，几乎可以在大约100行代码中的任何地方使用。在一项对我们的$1识别器、动态时间规整和Rubine分类器在用户提供的手势上进行比较的研究中，我们发现$1在仅加载1个模板的情况下获得了97%以上的准确率，而在3个以上加载的模板上获得了99%的准确率。这些结果与DTW几乎相同，但优于Rubine。此外，我们发现，在所有三种识别器中，用户平衡速度和准确性的中速手势比慢速或快速手势的识别效果更好。我们还讨论了模板或训练样本的数量对识别的影响，识别器的N-Best列表上的分数衰减，以及单个手势的结果。我们包含了$1识别器的详细伪代码，以帮助开发、检查、扩展和测试。
Although mobile, tablet, large display, and tabletop computers increasingly present opportunities for using pen, finger, and wand gestures in user interfaces, implementing gesture recognition largely has been the privilege of pattern matching experts, not user interface prototypers. Although some user interface libraries and toolkits offer gesture recognizers, such infrastructure is often unavailable in design-oriented environments like Flash, scripting environments like JavaScript, or brand new off-desktop prototyping environments. To enable novice programmers to incorporate gestures into their UI prototypes, we present a "$1 recognizer" that is easy, cheap, and usable almost anywhere in about 100 lines of code. In a study comparing our $1 recognizer, Dynamic Time Warping, and the Rubine classifier on user-supplied gestures, we found that $1 obtains over 97% accuracy with only 1 loaded template and 99% accuracy with 3+ loaded templates. These results were nearly identical to DTW and superior to Rubine. In addition, we found that medium-speed gestures, in which users balanced speed and accuracy, were recognized better than slow or fast gestures for all three recognizers. We also discuss the effect that the number of templates or training examples has on recognition, the score falloff along recognizers' N-best lists, and results for individual gestures. We include detailed pseudocode of the $1 recognizer to aid development, inspection, extension, and testing.