diff --git a/LICENSE b/LICENSE
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--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 perceptualui.org
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
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-# knuckletouch
+# KnuckleTouch
+This repository contains the data set and scripts for the MuC '19 paper on "KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning".
 
+## 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.
+
+This work can be cited as follows:
+<pre>
+@inproceedings{Schweigert:2019:KTE,
+title = {KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning},
+author = {Schweigert, Robin and Leusmann, Jan and Hagenmayer, Simon and Weiß, Maximilian and Le, Huy Viet and Mayer, Sven and Bulling, Andreas},
+doi = {10.1145/3340764.3340767},
+year = {2019},
+date = {2019-09-08},
+booktitle = {Mensch und Computer},
+series = {MuC '19},
+location = {Hamburg, Germany},
+publisher = {ACM},
+address = {New York, NY, USA},
+}
+</pre>