@inproceedings{Schweigert:2019:KTE,
 abstract = {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.},
 author = {Robin Schweigert and Jan Leusmann and Simon Hagenmayer and Maximilian Weiss and Huy Viet Le and Sven Mayer and Andreas Bulling},
 booktitle = {Mensch und Computer},
 date = {2019-09-08},
 doi = {10.1145/3340764.3340767},
 keywords = {capacitive sensing, interaction technique, mobile device, sensing, touch, touchscreen},
 pubstate = {published},
 series = {MuC '19},
 title = {KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning},
 tppubtype = {inproceedings},
 url = {http://sven-mayer.com/wp-content/uploads/2019/07/schweigert2019knuckletouch.pdf
https://www.youtube.com/watch?v=4U1daa7fCbY
https://git.perceptualui.org/public-projects/knuckletouch},
 year = {2019}
}