Predicting Finger-Touch Accuracy Based on the Dual Gaussian Distribution Model

Abstract

Modelling the accuracy of finger-touch target acquisition is crucial for designing touchscreen UI and for modeling more complex and higher level touch interaction behaviors. Despite its importance, there has been little theoretical work on creating such models. Building on the Dual Gaussian Distribution Model[3], we derived an accuracy model that predicts the success rate of target acquisition based on the target size. We evaluated the model by comparing the predicted success rates with empirical measures for three types of targets: 1-dimensional vertical, 1-dimensional horizontal, and 2-dimensional targets. The predictions matched the empirical data very well: the differences between predicted and observed success rates were under 5% for 4.8 mm and 7.2 mm targets, and under 10% for 2.4 mm targets. The evaluation results suggest that our simple model can reliably predict touch accuracy.

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

“…Although Drury's derivation to predict V and MT was based on a probabilistic model [17,20], success rate prediction models based on A and W have never been developed. If we can derive such a model, it would be of benefit to HCI and UI designs, similarly to the claims in related work [9,31,33,61]. In addition, because the steering law is valid for many human-machine interactions, including outside-GUI tasks, our model will potentially contribute to task-difficulty estimation for (e.g.)…”

Rethinking the Dual Gaussian Distribution Model for Predicting Touch Accuracy in On-screen-start Pointing Tasks

“…Besides the case of rapidly aimed movements, the error rate has also been investigated for tapping on a static Model 205:3 button within a given temporal window [30][31][32]. Despite the recent importance of finger-touch operations on smartphones and tablets, however, the only model for predicting the success rate while accounting for finger-touch ambiguity is the work by Bi and Zhai on pointing from an off-screen start [10]. It would be useful if we could extend the validity of their model to other applications.…”

“…Bi and Zhai then derived their success-rate prediction model [10]. Assuming a negligible correlation between the observed touch point values on the -and -axes (i.e., = 0) gives the following probability density function for the bivariate Gaussian distribution:…”

“…To predict how successfully users will tap a target, Bi and Zhai proposed a model that computes the success rate solely from the target size (or width ) for both 1D and 2D pointing tasks [10]. They reasonably limited their model's applicability to touch-pointing tasks starting off-screen, i.e., when a user's finger moves from a position outside the touch screen.…”

Modeling Two Dimensional Touch Pointing

“…Bi, Li and Zhai [4,5,6] identified this challenge, and proposed the Finger Fitts law [4] to address it. They derived their model by separating two sources of end point variance -those due to the absolute imprecision of finger touch (denoted by σ a 2 ) and those due to the speed-accuracy trade-off demonstrated in a pointing process (denoted by σ r 2 ).…”