Computing Touch-Point Ambiguity on Mobile Touchscreens for Modeling Target Selection Times

TL;DR

This paper investigates methods to measure finger tremor in touch-pointing models, revealing that parameter optimization is less effective for predicting user performance on mobile touchscreens.

Contribution

It provides a comparative analysis of measurement approaches for finger tremor, highlighting the limitations of parameter optimization in modeling touch-pointing times.

Findings

Parameter optimization is suboptimal for predicting touch performance.

Reanalysis of previous data supports the proposed conclusions.

Integrating experimental results improves understanding of touch-point ambiguity.

Abstract

Finger-Fitts law (FFitts law) is a model to predict touch-pointing times, modified from Fitts' law. It considers the absolute touch-point precision, or a finger tremor factor sigma_a, to decrease the admissible target area and thus increase the task difficulty. Among choices such as running an independent task or performing parameter optimization, there is no consensus on the best methodology to measure sigma_a. This inconsistency could be detrimental to HCI studies such as pointing technique evaluations and user group comparisons. By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute sigma_a. We found that the parameter optimization method is a suboptimal choice for predicting the performance.