@inproceedings{
 title = {Coherence in One-Shot Gesture Recognition for Human-Robot Interaction},
 type = {inproceedings},
 year = {2018},
 keywords = {gesture recognition,one-shot learning,robotics},
 pages = {75-76},
 volume = {Part F1351},
 id = {39291ba0-c081-3c15-90f4-35b698d61c66},
 created = {2021-06-04T19:36:49.433Z},
 file_attached = {false},
 profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},
 last_modified = {2021-06-07T19:16:59.462Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Cabrera2018c},
 folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},
 private_publication = {false},
 abstract = {An experiment was conducted where a robotic platform performs artificially generated gestures and both trained classifiers and human participants recognize. Classification accuracy is evaluated through a new metric of coherence in gesture recognition between humans and robots. Experimental results showed an average recognition performance of 89.2% for the trained classifiers and 92.5% for the participants. Coherence in one-shot gesture recognition was determined to be gamma = 93.8%. This new metric provides a quantifier for validating how realistic the robotic generated gestures are.},
 bibtype = {inproceedings},
 author = {Cabrera, M.E. Maria E. and Voyles, Richard M. R.M. and Wachs, J.P. Juan P.},
 doi = {10.1145/3173386.3176977},
 booktitle = {ACM/IEEE International Conference on Human-Robot Interaction}
}

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