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

TL;DR

This paper introduces the Skewed Dual Normal Distribution Model to accurately predict touch success rates for targets near screen edges, addressing a gap in existing models and demonstrating its effectiveness through multiple experiments.

Contribution

The paper presents a novel skewed distribution model that accounts for edge effects in touch accuracy prediction, improving upon prior models that neglected edge proximity.

Findings

Success rate increases when tapping near edges due to edge-adjacent strategies.

The model accurately predicts success rates across various edge-adjacent target conditions.

Experiments confirm the model's generalizability in different pointing tasks.

Abstract

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.