public-projects
/
knuckletouch
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updated README

This commit is contained in:
mayersn 2019-08-07 18:00:11 -04:00
parent 0fb6efadd5
commit b8b8930649
1 changed files with 7 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
README.md
View File

@ -1,5 +1,6 @@
# 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".
This repository contains the data set and scripts for the MuC '19 paper on "KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning". [YouTube](https://www.youtube.com/watch?v=akL3Ejx3bv8)
## 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.
@ -19,3 +20,8 @@ publisher = {ACM},
address = {New York, NY, USA},
}
</pre>
## Repository structure
* viewer/ Android example application, this requires a hacked LG Nexus 5
* python/ Evaluation code of all models described in the paper.