Using Machine Learning Concepts for Learning gestures
This study addresses a new challenge in the design of gesture based applications for multi-touch devices: to design gestures that users consider natural, understandable and easy to use.
The study aims to provide designers with an easy way to create gestures that does not require any programming skills. In order to learn custom gestures, we developed IGT, a machine learning tool based on the Gesture Toolkit that uses an approximation of the anti-unification modulo theory to learn based on samples of the gesture provided by the designer.
Images and Videos
Learning gestures for interacting with low-fidelity prototypes. Accepted to the Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’12). To be held June 22—29, 2012, in Zurich, Switzerland
NRC Institute for Biodiagnostics
Workshops and Symposiums
Surfnet workshop 2011 – Tutorial: Using Machine Learning to Recognize Gestures
Research Focus Area(s)
- Frank Maurer (Supervisor)
- Tulio Alcantara (MSc Student)