通过单击或双击预测降低单击延迟
Single-tap Latency Reduction with Single- or Double- tap Prediction
AI总结：
本文提出基于机器学习的点击预测方法PredicTaps，无需等待数百毫秒即可判断单击或双击，将单击延迟从150-500毫秒降至12-17.6毫秒，且不降低可用性。
摘要：
触摸表面广泛应用于智能手机、平板电脑和笔记本电脑（触摸板），单击和双击是其中最基本、最常见的操作。单击或双击的检测会导致单击延迟问题，这构成了触摸输入灵敏度方面的瓶颈。为了降低单击延迟，我们提出了一种新颖的基于机器学习的点击预测方法，名为PredicTaps。我们的方法预测检测到的点击是单击还是双击的第一次接触，而无需等待传统上所需的数百毫秒。我们展示了三项评估和一项用户评估，证明了该方法在两种形态因素（触摸板和智能手机）上对各种点击情况具有广泛的适用性和可用性。结果表明，PredicTaps在不降低可用性的情况下，将笔记本电脑上的单击延迟从150-500毫秒降低到12毫秒，将智能手机上的单击延迟降低到17.6毫秒。
英文摘要：
Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce the single-tap latency, we propose a novel machine-learning-based tap prediction method called PredicTaps. Our method predicts whether a detected tap is a single tap or the first contact of a double tap without having to wait for the hundreds of milliseconds conventionally required. We present three evaluations and one user evaluation that demonstrate its broad applicability and usability for various tap situations on two form factors (touchpad and smartphone). The results showed PredicTaps reduces the single-tap latency from 150-500 ms to 12 ms on laptops and to 17.6 ms on smartphones without reducing usability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
