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

TL;DR

This paper introduces the Skewed Dual Normal Distribution Model to accurately predict 1D touch success rates for targets near screen edges, addressing a gap in existing models and aiding UI design.

Contribution

It presents a novel skewed distribution model that accounts for edge effects on touch accuracy, extending predictive capabilities to edge-adjacent targets.

Findings

Success rate improves when tapping targets at the edge.

Model accurately predicts success rates near edges.

Edge-adjacent tapping can be a successful strategy.

Abstract

Typical success-rate prediction models for tapping exclude targets near screen edges; however, design constraints often force such placements. Additionally, in scrollable UIs any element can move close to an edge. In this work, we model how target--edge distance affects 1D 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 of two smartphone experiments 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.'' By accounting for skew, our model predicts success rates across a wide range of conditions, including edge-adjacent targets, thus extending coverage to the whole screen and informing UI design support tools.