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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8309784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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