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Tobias Boceck, Sascha Sprott, Huy Viet Le, Sven Mayer
Force Touch Detection on Capacitive Sensors using Deep Neural Networks
Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI'19), ACM, 2019-10-01 (bib)

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.