ContextType: using hand posture information to improve mobile touch screen text entry

Authors:
Mayank Goel;Alex Jansen;Travis Mandel;Shwetak N. Patel;Jacob O. Wobbrock
Affiliations:
University of Washington, Seattle, Washington, USA;University of Washington, Seattle, Washington, USA;University of Washington, Seattle, Washington, USA;University of Washington, Seattle, Washington, USA;University of Washington, Seattle, Washington, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2013

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Abstract

The challenge of mobile text entry is exacerbated as mobile devices are used in a number of situations and with a number of hand postures. We introduce ContextType, an adaptive text entry system that leverages information about a user's hand posture (using two thumbs, the left thumb, the right thumb, or the index finger) to improve mobile touch screen text entry. ContextType switches between various keyboard models based on hand posture inference while typing. ContextType combines the user's posture-specific touch pattern information with a language model to classify the user's touch events as pressed keys. To create our models, we collected usage patterns from 16 participants in each of the four postures. In a subsequent study with the same 16 participants comparing ContextType to a control condition, ContextType reduced total text entry error rate by 20.6%.