Single-tap Latency Reduction with Single- or Double- tap Prediction

TL;DR

This paper introduces PredicTaps, a machine learning method that predicts tap type to significantly reduce latency in touch interfaces on laptops and smartphones without compromising usability.

Contribution

It presents a novel tap prediction technique that minimizes latency by identifying tap types instantly, improving responsiveness in touch-based devices.

Findings

Reduces laptop tap latency from 150-500 ms to 12 ms.

Reduces smartphone tap latency from 150-500 ms to 17.6 ms.

Maintains usability despite latency reduction.

Abstract

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.