Google Research

Analysis and Modeling of Grid Performance on Touchscreen Mobile Devices

CHI'18 (2018)

Abstract

Touchscreen mobile devices can afford rich interaction behaviors but they are complex to model. Scrollable two-dimensional grids are a common user interface on mobile devices that allow users to access a large number of items on a small screen by direct touch. By analyzing touch input and eye gaze of users during grid interaction, we reveal how multiple performance components come into play in such a task, including navigation, visual search and pointing. These findings inspired us to design a novel predictive model that combines these components for modeling grid tasks. We realized these model components by employing both traditional analytical methods and data-driven machine learning approaches. In addition to showing high accuracy achieved by our model in predicting human performance on a test dataset, we demonstrate how such a model can lead to a significant reduction in interaction time when used in a predictive user interface.

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