@inproceedings{Kumar:2019:ITI,
 abstract = {Touchscreens combine input and output in a single interface. While this enables an intuitive interaction and dynamic user interfaces, the fat-finger problem and the resulting occlusions still impact the input accuracy. Previous work presented approaches to improve the touch accuracy by involving visual features on the top side of fingers, as well as static compensation functions. While the former is not applicable on recent mobile devices as the top side of a finger cannot be tracked, compensation functions do not take properties such as finger angle into account. In this work, we present a datadriven approach to estimate the 2D touch position on commodity mutual capacitive touchscreens which increases the touch accuracy by 23.0 % over recently implemented approaches.},
 address = {New York, NY, USA},
 author = {Abinaya Kumar and Aishwarya Radjesh and Sven Mayer and Huy Viet Le},
 booktitle = {Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems},
 date = {2019-05-04},
 doi = {10.1145/3290607.3312928},
 keywords = {deep learning, machine learning, mobile device},
 publisher = {ACM},
 pubstate = {published},
 series = {CHI'19 EA},
 title = {Improving the Input Accuracy of Touchscreens using Deep Learning},
 tppubtype = {inproceedings},
 url = {http://sven-mayer.com/wp-content/uploads/2019/02/kumar2018accuracy.pdf
https://github.com/interactionlab/improving-touch-accuracy},
 year = {2019}
}