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

摘要

Gesture typing is a method of text entry that is ergonomically well-suited tothe form factor of touchscreen devices and allows for much faster input thantapping each letter individually. The QWERTY keyboard was, however, notdesigned with gesture input in mind and its particular layout results in a highfrequency of gesture recognition errors. In this paper, we describe a newapproach to quantifying the frequency of gesture input recognition errorsthrough 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 performMonte Carlo evaluations of keyboard error rates or any other arbitrary metric.An open source framework, Dodona, is also provided that allows for thesetechniques to be easily employed and customized in the evaluation of a widespectrum of possible keyboards and input methods. Finally, we perform anoptimization procedure over permutations of the QWERTY keyboard to demonstratethe effectiveness of this approach and describe ways that future analyses canbuild upon these results.