Name
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 data-driven approach to estimate the 2D touch position on commodity mutual capacitive touchscreens which increases the touch accuracy by 23.0% over recently implemented approaches.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3290607.3312928 | CHI Extended Abstracts |
Keywords | Field | DocType |
capacitive image, deep learning, input accuracy, smartphone, targeting, touch input, touchscreen | Computer science,Touchscreen,Input/output,Capacitive sensing,Human–computer interaction,Mobile device,Artificial intelligence,Deep learning,User interface | Conference |
ISBN | Citations | PageRank |
978-1-4503-5971-9 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abinaya Kumar | 1 | 1 | 0.35 |
| Aishwarya Radjesh | 2 | 1 | 0.35 |
| Sven Mayer | 3 | 188 | 27.30 |
| Huy Viet Le | 4 | 95 | 13.02 |