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

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.

Figures (83)

• Figure 1: The proposed Skewed Dual Normal Distribution Model assumes that the tap-coordinate distribution is skewed by the presence of a screen edge on one side of the target and uses the cumulative distribution function of the skew-normal distribution to estimate tap success rate. (a) When the target is sufficiently far from the screen edge, the tap-coordinate distribution is normal (Gaussian). (b) When the target is near the screen edge, the tap-coordinate distribution becomes skew-normal.
• Figure 2: Touch event near edge
• Figure 3: Distributional changes with edge distance
• Figure 4: Computing $\mathit{SR}$ by CDF
• Figure 6: The difference between raised and flat bezels