カンファレンス (国際) Single-tap Latency Reduction with Single- or Double- tap Prediction

Naoto Nishida* (The University of Tokyo) , Kaori Ikematsu*, Junichi Sato, Shota Yamanaka, Kota Tsubouchi, *co-first authors

The ACM International Conference on Mobile Human-Computer Interaction (MobileHCI2023)


Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and standard operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck regarding the sensitivity of touch inputs. To reduce the single-tap latency, we propose a novel machine-learning-based tap recognition 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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