research-article

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

Authors:

Kaori Ikematsu,

Shota Yamanaka,

Kota TsubouchiAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 7, Issue MHCI

Article No.: 224, Pages 1 - 26

https://doi.org/10.1145/3604271

Published: 13 September 2023 Publication History

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.

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Cited By

Fu RDong QLiu Y(2024)Performance and Reliability of Touch Screen: A Game-Oriented Research2024 5th International Conference on Machine Learning and Human-Computer Interaction (MLHMI)10.1109/MLHMI63000.2024.00016(31-36)Online publication date: 14-Mar-2024
https://doi.org/10.1109/MLHMI63000.2024.00016

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 7, Issue MHCI

MHCI

September 2023

1017 pages

EISSN:2573-0142

DOI:10.1145/3624512

Editor:
Jeff Nichols
Apple Inc., United States

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Published: 13 September 2023

Published in PACMHCI Volume 7, Issue MHCI

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Fu RDong QLiu Y(2024)Performance and Reliability of Touch Screen: A Game-Oriented Research2024 5th International Conference on Machine Learning and Human-Computer Interaction (MLHMI)10.1109/MLHMI63000.2024.00016(31-36)Online publication date: 14-Mar-2024
https://doi.org/10.1109/MLHMI63000.2024.00016

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