This repository contains the data and model for the MobileHCI '19 LBR paper on "Force Touch Detection on Capacitive Sensors using Deep Neural Networks".

As the touchscreen is the most successful input method of current mobile devices, the importance to transmit more in- formation 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 in- creases production cost and hardware complexity. More- over, 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.

This work can be cited as follows:

@inproceedings{Boceck:2019:ForceTouchDetection,
title = {Force Touch Detection on Capacitive Sensors using Deep Neural Networks},
author = {Boceck, Tobias and Sprott, Sascha and Le, Huy Viet and Mayer, Sven},
year = {2019},
date = {2019-10-01},
booktitle = {Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct},
publisher = {ACM},
address = {New York, NY, USA},
series = {MobileHCI '19}
}