@article{
 title = {uWave: Accelerometer-based personalized gesture recognition and its applications},
 type = {article},
 year = {2009},
 identifiers = {[object Object]},
 keywords = {accelerometer,authentication,dtw,gesture,gesture-recognition},
 pages = {657-675},
 volume = {5},
 websites = {http://dx.doi.org/10.1016/j.pmcj.2009.07.007},
 month = {12},
 id = {ad065abf-a1fa-36a3-8ebd-ed6f4d1161b0},
 created = {2018-07-12T21:31:01.719Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:31:01.719Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {liu:uwave09},
 source_type = {article},
 notes = {earlier version appeared in PerCom 2009},
 private_publication = {false},
 abstract = {The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures. We evaluate uWave using a large gesture library with over 4000 samples for eight gesture patterns collected from eight users over one month. uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. We also present applications of uWave in gesture-based user authentication and interaction with 3D mobile user interfaces. In particular, we report a series of user studies that evaluates the feasibility and usability of lightweight user authentication. Our evaluation shows both the strength and limitations of gesture-based user authentication.},
 bibtype = {article},
 author = {Liu, Jiayang and Zhong, Lin and Wickramasuriya, Jehan and Vasudevan, Venu},
 journal = {Pervasive and Mobile Computing},
 number = {6}
}

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