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LICENSE
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LICENSE
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MIT License
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Copyright (c) 2019 perceptualui.org
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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# knuckletouch
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# KnuckleTouch
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This repository contains the data set and scripts for the MuC '19 paper on "KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning".
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## Abstract
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While mobile devices have become essential for social communication and have paved the way for work on the go, their interactive capabilities are still limited to simple touch input. A promising enhancement for touch interaction is knuckle input but recognizing knuckle gestures robustly and accurately remains challenging. We present a method to differentiate between 17 finger and knuckle gestures based on a long short-term memory (LSTM) machine learning model. Furthermore, we introduce an open source approach that is ready-to-deploy on commodity touch-based devices. The model was trained on a new dataset that we collected in a mobile interaction study with 18 participants. We show that our method can achieve an accuracy of 86.8% on recognizing one of the 17 gestures and an accuracy of 94.6% to differentiate between finger and knuckle. In our evaluation study, we validate our models and found that the LSTM gestures recognizing archived an accuracy of 88.6%. We show that KnuckleTouch can be used to improve the input expressiveness and to provide shortcuts to frequently used functions.
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This work can be cited as follows:
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<pre>
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@inproceedings{Schweigert:2019:KTE,
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title = {KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning},
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author = {Schweigert, Robin and Leusmann, Jan and Hagenmayer, Simon and Weiß, Maximilian and Le, Huy Viet and Mayer, Sven and Bulling, Andreas},
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doi = {10.1145/3340764.3340767},
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year = {2019},
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date = {2019-09-08},
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booktitle = {Mensch und Computer},
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series = {MuC '19},
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location = {Hamburg, Germany},
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publisher = {ACM},
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address = {New York, NY, USA},
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}
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</pre>
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