A recognition safety net: bi-level threshold recognition for mobile motion gesturesMatei Negulescu, Jaime Ruiz, and Edward Lank

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.

Authors

  • Headshot of Jaime Ruiz wearing a HololensJaime Ruiz

    • As well as: Matei Negulescu and Edward Lank

Citation

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). Association for Computing Machinery, New York, NY, USA, 147–150. https://doi.org/10.1145/2371574.2371598

Bibtex

@inproceedings{10.1145/2371574.2371598,
author = {Negulescu, Matei and Ruiz, Jaime and Lank, Edward},
title = {A Recognition Safety Net: Bi-Level Threshold Recognition for Mobile Motion Gestures},
year = {2012},
isbn = {9781450311052},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2371574.2371598},
doi = {10.1145/2371574.2371598},
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.},
booktitle = {Proceedings of the 14th International Conference on Human-Computer Interaction with Mobile Devices and Services},
pages = {147–150},
numpages = {4},
keywords = {bi-level thresholding, safety net, motion gestures},
location = {San Francisco, California, USA},
series = {MobileHCI '12}
}

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