Learning gestures for interacting with low-fidelity prototypes

Abstract

This paper presents an approach to help designers create their own application-specific gestures and evaluate them in user-studies based on low fidelity prototypes of the application they are designing. In order to learn custom gestures, we developed a machine learning tool that uses an anti-unification algorithm to learn based on samples of the gesture provided by the designer.