RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID

As the basis of human-computer interaction (HCI), gesture recognition interprets user-performed gestures as commands, followed by the content execution expressed by users’ gestures. Gesture recognition through wireless signals denotes a novel branch of human perception. Despite the recent...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhixiong Yang, Xu Liu, Zijian Li, Bo Yuan, Yajun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Gesture recognition RFID tags Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ff763d7e65e445c49a6b79a397467cad
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ff763d7e65e445c49a6b79a397467cad
record_format	dspace
spelling	oai:doaj.org-article:ff763d7e65e445c49a6b79a397467cad2021-11-26T00:01:56ZRF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID2169-353610.1109/ACCESS.2021.3128293https://doaj.org/article/ff763d7e65e445c49a6b79a397467cad2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615049/https://doaj.org/toc/2169-3536As the basis of human-computer interaction (HCI), gesture recognition interprets user-performed gestures as commands, followed by the content execution expressed by users’ gestures. Gesture recognition through wireless signals denotes a novel branch of human perception. Despite the recent popularity of Radio Frequency Identification (RFID) following specific advantages (lightweight, low-cost, and universality), several intricacies remain unresolved in RFID sensing research. First, most studies performed simplified body movements assessments instead of identifying complex and fine-grained or subtle gestures. Second, users require extensive training in a novel discipline to collect training data in a specific pattern. Given the paucity of an intuitive and effective means of identifying user gestures, the RF-E-letter proposed in this study denotes an RFID recognition system for complex, fine-grained, and domain-independent gestures. A multi-label array was utilized to gather gesture signals. Fine-grained gesture data could be obtained pre-processing with a novel data-processing method. Seemingly irregular RFID phase data could be converted into intuitive images for the deep learning module input as convolutional neural networks (CNNs) encompass automatic extraction characteristics for complex space-time features. The average accuracy of new environments for novel users is 95.6% and 96.6%, respectively (significantly better than current RFID-based solutions), thus demonstrating effectiveness and versatility.Zhixiong YangXu LiuZijian LiBo YuanYajun ZhangIEEEarticleGesture recognitionRFIDtagsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155260-155273 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Gesture recognition RFID tags Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Gesture recognition RFID tags Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zhixiong Yang Xu Liu Zijian Li Bo Yuan Yajun Zhang RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID
description	As the basis of human-computer interaction (HCI), gesture recognition interprets user-performed gestures as commands, followed by the content execution expressed by users’ gestures. Gesture recognition through wireless signals denotes a novel branch of human perception. Despite the recent popularity of Radio Frequency Identification (RFID) following specific advantages (lightweight, low-cost, and universality), several intricacies remain unresolved in RFID sensing research. First, most studies performed simplified body movements assessments instead of identifying complex and fine-grained or subtle gestures. Second, users require extensive training in a novel discipline to collect training data in a specific pattern. Given the paucity of an intuitive and effective means of identifying user gestures, the RF-E-letter proposed in this study denotes an RFID recognition system for complex, fine-grained, and domain-independent gestures. A multi-label array was utilized to gather gesture signals. Fine-grained gesture data could be obtained pre-processing with a novel data-processing method. Seemingly irregular RFID phase data could be converted into intuitive images for the deep learning module input as convolutional neural networks (CNNs) encompass automatic extraction characteristics for complex space-time features. The average accuracy of new environments for novel users is 95.6% and 96.6%, respectively (significantly better than current RFID-based solutions), thus demonstrating effectiveness and versatility.
format	article
author	Zhixiong Yang Xu Liu Zijian Li Bo Yuan Yajun Zhang
author_facet	Zhixiong Yang Xu Liu Zijian Li Bo Yuan Yajun Zhang
author_sort	Zhixiong Yang
title	RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID
title_short	RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID
title_full	RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID
title_fullStr	RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID
title_full_unstemmed	RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID
title_sort	rf-eletter: a cross-domain english letter recognition system based on rfid
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/ff763d7e65e445c49a6b79a397467cad
work_keys_str_mv	AT zhixiongyang rfeletteracrossdomainenglishletterrecognitionsystembasedonrfid AT xuliu rfeletteracrossdomainenglishletterrecognitionsystembasedonrfid AT zijianli rfeletteracrossdomainenglishletterrecognitionsystembasedonrfid AT boyuan rfeletteracrossdomainenglishletterrecognitionsystembasedonrfid AT yajunzhang rfeletteracrossdomainenglishletterrecognitionsystembasedonrfid
_version_	1718410007530176512

RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID

Ejemplares similares