AI

Bayesian Touch - A Statistic Criterion of Target Selection with Finger Touch

Abstract

To improve the accuracy of target selection for finger touch, we conceptualize finger touch input as an uncertain process, and derive a statistical target selection riterion, Bayesian Touch Criterion, from combining the basic Bayes’ rule of probability with the generalized dual Gaussian distribution hypothesis of finger touch. Bayesian Touch Criterion states that the selected target is the candidate with the shortest Bayesian Touch Distance to the touch point, which is computed from the touch point to target center distance and the size of the target. We give the derivation of the Bayesian touch criterion and its empirical evaluation with two experiments. The results show for 2D circular target selection, Bayesian Touch Criterion is significantly more accurate than the commonly used Visual Boundary Criterion (i.e., a target is selected if and only if the touch point falls within its boundary) and its two variants.