A recognition safety net: bi-level threshold recognition for mobile motion gestures

Authors:
Matei Negulescu;Jaime Ruiz;Edward Lank
Affiliations:
University of Waterloo, Waterloo, Ontario, Canada;Colorado State University, Fort Collins, CO, USA;University of Waterloo, Waterloo, Ontario, Canada
Venue:
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
Year:
2012

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Abstract

Designers of motion gestures for mobile devices face the difficult challenge of building a recognizer that can separate gestural input from motion noise. A threshold value is often used to classify motion and effectively balances the rates of false positives and false negatives. We present a bi-level threshold recognition technique designed to lower the rate of recognition failures by accepting either a tightly thresholded gesture or two consecutive possible gestures recognized by a relaxed model. Evaluation of the technique demonstrates that the technique can aid in recognition for users who have trouble performing motion gestures. Lastly, we suggest the use of bi-level thresholding to scaffold the learning of gestures.