Shota Yamanaka

Naoto Nishida, Kaori Ikematsu, Junichi Sato et al.

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.

Nobuhito Kasahara, Shota Yamanaka, Homei Miyashita

Typical success-rate prediction models for tapping exclude targets near screen edges. However, design constraints often force such placements, and in scrollable user interfaces, any element can move close to the screen edges. In this work, we model how target-edge distance affects touch pointing accuracy. We propose the Skewed Dual Normal Distribution Model, which assumes the tap-coordinate distribution is skewed by a nearby edge. The results showed that as targets approached the edge, the distribution's peak shifted toward the edge, and its tail extended away. In contrast to prior reports, the success rate improved when the target touched the edge, suggesting a strategy of ``tapping the target together with the edge.'' Our model predicts success rates across a wide range of conditions, including edge-adjacent targets. Through three experiments of horizontal, vertical, and 2D pointing, we demonstrated the generalizability and utility of our proposed model.

Ryuto Tomihari, Taiki Kinoshita, Yosuke Oba et al.

In projectors and head-mounted displays, an out-of-focus image appears blurred. Even when a display itself is in focus, computer operation may be hindered if the display is far from the user or if a user has poor visual acuity, because the user cannot see the screen clearly. In this study, we conducted an experiment in which participants performed a pointing task under blurred display conditions and investigated the relationship between blur strength and user performance. The results showed that movement time and error rate increased as blur became stronger, and that the effect of blur on movement time was larger when targets were smaller. We further showed that movement time can be estimated with high accuracy by a model that improves on Fitts' law. In a follow-up experiment to examine the applicability of this model, we adjusted target size for each participant and showed that the effect of blur level on movement time could be reduced. These findings suggest potential use in tools that adapt user interfaces to users' visual acuity.

Shota Yamanaka, Hiroki Usuba

Finger-Fitts law (FFitts law) is a model to predict touch-pointing times, modified from Fitts' law. It considers the absolute touch-point precision, or a finger tremor factor sigma_a, to decrease the admissible target area and thus increase the task difficulty. Among choices such as running an independent task or performing parameter optimization, there is no consensus on the best methodology to measure sigma_a. This inconsistency could be detrimental to HCI studies such as pointing technique evaluations and user group comparisons. By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute sigma_a. We found that the parameter optimization method is a suboptimal choice for predicting the performance.