Abstract
Touchscreens have been studied and developed for a long time to provide user-friendly and intuitive interfaces on displays. This paper describes the touchscreen technologies in four categories of resistive, capacitive, acoustic wave, and optical methods. Then, it addresses the main studies of SNR improvement and stylus support on the capacitive touchscreens that have been widely adopted in most consumer electronics such as smartphones, tablet PCs, and notebook PCs. In addition, the machine learning approaches for capacitive touchscreens are explained in four applications of user identification/authentication, gesture detection, accuracy improvement, and input discrimination.
Highlights
Human beings collect a lot of information through their eyes, and many displays around us play a key role to transfer this visual information
This paper provides a unified and broader view of the touchscreen technologies with the detailed explanation and machine learning (ML) approaches in various scenarios
Meng et al [139] constructed 21 features such as average touch movement speeds for eight directions, fractions of touch movements for eight directions, average single-touch time, average multi-touch time, number of touch movements per session, number of single-touch events per session, and number of multi-touch events per session. They evaluated the performance of decision tree (DT), naive Bayes (NB), Kstar, radial basis function network (RBFN), and back propagation neural network (BPNN), leading to the conclusion that RBFN showed the best performance with false accept ratio (FAR) and false reject ratio (FRR) of 7.08% and
Summary
Human beings collect a lot of information through their eyes, and many displays around us play a key role to transfer this visual information. There have been efforts to integrate machine learning (ML) approaches into touchscreen technologies These ML networks are employed to add extra input tools, to improve the touch-sensing performance, to support the user identification/authentication, to discriminate finger-touches from others, and to capture the gestures [135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
