@inproceedings{Boceck:2019:ForceTouch,
 abstract = {As the touchscreen is the most successful input method of current mobile devices, the importance to transmit more information per touch is raising. A wide range of approaches has been presented to enhance the richness of a single touch. With Apple's 3D Touch, they successfully introduce pressure as a new input dimension into consumer devices. However, they are using a new sensing layer, which increases production cost and hardware complexity. Moreover, users have to upgrade their phones to use the new feature. In contrast, with this work, we introduce a strategy to acquire the pressure measurements from the mutual capacitive sensor, which is used in the majority of today's touch devices. We present a data collection study in which we collect capacitive images where participants apply different pressure levels. We then train a Deep Neural Network (DNN) to estimate the pressure allowing for force touch detection. As a result, we present a model which enables estimating the pressure with a mean error of 369.0g.},
 address = {New York, NY, USA},
 author = {Tobias Boceck and Sascha Sprott and Huy Viet Le and Sven Mayer},
 booktitle = {Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services},
 date = {2019-10-01},
 doi = {10.1145/3338286.3344389},
 isbn = {978-1-4503-6825-4/19/10},
 keywords = {capacitive sensing, force touch, mobile device, pressure},
 pages = {6},
 publisher = {ACM},
 pubstate = {published},
 series = {MobileHCI'19},
 title = {Force Touch Detection on Capacitive Sensors using Deep Neural Networks},
 tppubtype = {inproceedings},
 url = {http://sven-mayer.com/wp-content/uploads/2019/07/boceck2019forcetouch.pdf
https://github.com/interactionlab/ForceTouchDetection},
 year = {2019}
}