Schweigert, RobinLeusmann, JanHagenmayer, SimonWeiß, MaximilianLe, Huy VietMayer, SvenBulling, AndreasAlt, FlorianBulling, AndreasDöring, Tanja2019-08-222019-08-222019https://dl.gi.de/handle/20.500.12116/24596While 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.enKuckleTouchfingerknuckleinputdata setdeep neural networksconvolutional neural networklong short term memoryText/Conference Paper10.1145/3340764.3340767