Modeling Touch Point Distribution with Rotational Dual Gaussian Model.
Modeling Touch Point Distribution with Rotational Dual Gaussian Model.
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DOI：
10.1145/3472749.3474816
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发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Bi X
中科院分区：
文献类型：
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作者：
Ma Y;Zhai S;Ramakrishnan IV;Bi X
触摸点分布模型是设计触摸屏界面的重要工具。在本文中，我们研究手指移动方向如何影响触摸点分布，以及如何在建模中考虑它。我们提出了旋转双高斯模型，双高斯模型的细化和推广，占手指移动方向预测触摸点分布。在该模型中，触摸点分布的预测椭圆的长轴沿着手指移动方向，短轴垂直于手指移动方向。我们还建议使用投影目标宽度和高度来代替名义目标宽度和高度来对触摸点分布进行建模。在三个实验数据集上的评估表明，新模型反映了触摸点分布沿手指移动方向沿着拉长的观察结果，在所有预测测试中均优于原双高斯模型。与原双高斯模型相比，旋转双高斯模型将触摸错误率预测的RMSE从8.49%降低到4.95%，更准确地预测目标获取中的触摸点分布。使用旋转双高斯模型还可以提高智能手表上的软键盘解码精度。
Touch point distribution models are important tools for designing touchscreen interfaces. In this paper, we investigate how the finger movement direction affects the touch point distribution, and how to account for it in modeling. We propose the Rotational Dual Gaussian model, a refinement and generalization of the Dual Gaussian model, to account for the finger movement direction in predicting touch point distribution. In this model, the major axis of the prediction ellipse of the touch point distribution is along the finger movement direction, and the minor axis is perpendicular to the finger movement direction. We also propose using projected target width and height, in lieu of nominal target width and height to model touch point distribution. Evaluation on three empirical datasets shows that the new model reflects the observation that the touch point distribution is elongated along the finger movement direction, and outperforms the original Dual Gaussian Model in all prediction tests. Compared with the original Dual Gaussian model, the Rotational Dual Gaussian model reduces the RMSE of touch error rate prediction from 8.49% to 4.95%, and more accurately predicts the touch point distribution in target acquisition. Using the Rotational Dual Gaussian model can also improve the soft keyboard decoding accuracy on smartwatches.