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.

Matei Negulescu, Jaime Ruiz, and Edward Lank. 2012. A recognition safety net: bi-level threshold recognition for mobile motion gestures. In Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services (MobileHCI '12). ACM, New York, NY, USA, 147-150.

@inproceedings{Negulescu:2012:RSN:2371574.2371598,
 author = {Negulescu, Matei and Ruiz, Jaime and Lank, Edward},
 title = {A Recognition Safety Net: Bi-level Threshold Recognition for Mobile Motion Gestures},
 booktitle = {Proceedings of the 14th International Conference on Human-computer Interaction with Mobile Devices and Services},
 series = {MobileHCI '12},
 year = {2012},
 isbn = {978-1-4503-1105-2},
 location = {San Francisco, California, USA},
 pages = {147--150},
 numpages = {4},
 url = {http://doi.acm.org/10.1145/2371574.2371598},
 doi = {10.1145/2371574.2371598},
 acmid = {2371598},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {bi-level thresholding, motion gestures, safety net},
}