A Monte Carlo Simulation Approach for Quantitatively Evaluating Keyboard Layouts for Gesture Input

TL;DR

This paper introduces a Monte Carlo simulation framework to evaluate and optimize keyboard layouts for gesture input, addressing recognition errors and improving input efficiency on touchscreen devices.

Contribution

It presents a novel simulation methodology and open-source tool for quantitatively assessing and optimizing gesture keyboard layouts, including a case study on QWERTY permutations.

Findings

Effective modeling of gesture input errors

Demonstrated optimization of keyboard layouts

Open-source framework for further research

Abstract

Gesture typing is a method of text entry that is ergonomically well-suited to the form factor of touchscreen devices and allows for much faster input than tapping each letter individually. The QWERTY keyboard was, however, not designed with gesture input in mind and its particular layout results in a high frequency of gesture recognition errors. In this paper, we describe a new approach to quantifying the frequency of gesture input recognition errors through the use of modeling and simulating realistically imperfect user input. We introduce new methodologies for modeling randomized gesture inputs, efficiently reconstructing words from gestures on arbitrary keyboard layouts, and using these in conjunction with a frequency weighted lexicon to perform Monte Carlo evaluations of keyboard error rates or any other arbitrary metric. An open source framework, Dodona, is also provided that allows for these techniques to be easily employed and customized in the evaluation of a wide spectrum of possible keyboards and input methods. Finally, we perform an optimization procedure over permutations of the QWERTY keyboard to demonstrate the effectiveness of this approach and describe ways that future analyses can build upon these results.