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Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches

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 im...

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Autores principales: Nam, Hyoungsik, Seol, Ki-Hyuk, Lee, Junhee, Cho, Hyeonseong, Jung, Sang Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309784/
https://www.ncbi.nlm.nih.gov/pubmed/34300514
http://dx.doi.org/10.3390/s21144776
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author Nam, Hyoungsik
Seol, Ki-Hyuk
Lee, Junhee
Cho, Hyeonseong
Jung, Sang Won
author_facet Nam, Hyoungsik
Seol, Ki-Hyuk
Lee, Junhee
Cho, Hyeonseong
Jung, Sang Won
author_sort Nam, Hyoungsik
collection PubMed
description 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.
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spelling pubmed-83097842021-07-25 Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches Nam, Hyoungsik Seol, Ki-Hyuk Lee, Junhee Cho, Hyeonseong Jung, Sang Won Sensors (Basel) Review 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. MDPI 2021-07-13 /pmc/articles/PMC8309784/ /pubmed/34300514 http://dx.doi.org/10.3390/s21144776 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Nam, Hyoungsik
Seol, Ki-Hyuk
Lee, Junhee
Cho, Hyeonseong
Jung, Sang Won
Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches
title Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches
title_full Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches
title_fullStr Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches
title_full_unstemmed Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches
title_short Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches
title_sort review of capacitive touchscreen technologies: overview, research trends, and machine learning approaches
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309784/
https://www.ncbi.nlm.nih.gov/pubmed/34300514
http://dx.doi.org/10.3390/s21144776
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