activity
20172026
most citedInteraction Design of Dwell Selection Toward Gaze-based AR/VR Interaction

11 citations · 19 across the 16 of their papers we have counts for

collaborators
Showing cs.HCShow all

12 papers · 1 filter

cs.HC2026

Blur Effects on User Performance in Target-Pointing Tasks

Ryuto Tomihari, Taiki Kinoshita, Yosuke Oba +2

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 fro…

cs.HC2026

Skewed Dual Normal Distribution Model: Predicting Touch Pointing Success Rates for Targets Near Screen Edges and Corners

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, an…

cs.HC2026★ 1 cited

Skewed Dual Normal Distribution Model: Predicting 1D Touch Pointing Success Rate for Targets Near Screen Edges

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. Additionally, in scrollable UIs any e…

cs.HC2026★ 1 cited

Improving Data Quality via Pre-Task Participant Screening in Crowdsourced GUI Experiments

Takaya Miyama, Satoshi Nakamura, Shota Yamanaka

In crowdsourced user experiments that collect performance data from graphical user interface (GUI) interactions, some participants ignore instructions or act carelessly, threatenin…

cs.HC2026★ 1 cited

Normalizing Speed-accuracy Biases in 2D Pointing Tasks with Better Calculation of Effective Target Widths

Shota Yamanaka, I. Scott MacKenzie

For evaluations of 2D target selection using Fitts' law, ISO 9241-411 recommends using the effective target width (W_e) calculated using the univariate standard deviation of select…

cs.HC2026

A Tool for Estimating Success Rates of Raycasting-Based Object Selection in Virtual Reality

Tatsuya Okuno, Haruto Shimizu, Nobuhito Kasahara +3

As XR devices become widespread, 3D interaction has become commonplace, and UI developers are increasingly required to consider usability to deliver better user experiences. The HC…

cs.HC2024

Tappy Plugin for Figma: Predicting Tap Success Rates of User-Interface Elements under Development for Smartphones

Shota Yamanaka, Hiroki Usuba, Junichi Sato +3

Tapping buttons and hyperlinks on smartphones is a fundamental operation, but users sometimes fail to tap user-interface (UI) elements. Such mistakes degrade usability, and thus it…

cs.HC2024★ 5 cited

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

Naoto Nishida, Kaori Ikematsu, Junichi Sato +2

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 o…

cs.HC2024

Tappy: Predicting Tap Accuracy of User-Interface Elements by Reverse-Engineering Webpage Structures

Hiroki Usuba, Junichi Sato, Naomi Sasaya +2

Selecting a UI element is a fundamental operation on webpages, and the ease of tapping a target object has a significant impact on usability. It is thus important to analyze existi…

cs.HC2023

Error-rate Prediction for Mouse-based Rectangular-target Pointing with no Knowledge of Movement Angles

Shota Yamanaka

In rectangular-target pointing, movement angles towards targets are known to affect error rates. When designers determine target sizes, however, they would not know the frequencies…

cs.HC2022★ 11 cited

Interaction Design of Dwell Selection Toward Gaze-based AR/VR Interaction

Toshiya Isomoto, Shota Yamanaka, Buntarou Shizuki

In this paper, we first position the current dwell selection among gaze-based interactions and its advantages against head-gaze selection, which is the mainstream interface for HMD…

cs.HC2021

Computing Touch-Point Ambiguity on Mobile Touchscreens for Modeling Target Selection Times

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…