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ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition
This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform sur...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506965/ https://www.ncbi.nlm.nih.gov/pubmed/32872287 http://dx.doi.org/10.3390/s20174878 |
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author | He, Han Chen, Xiaochen Mehmood, Adnan Raivio, Leevi Huttunen, Heikki Raumonen, Pasi Virkki, Johanna |
author_facet | He, Han Chen, Xiaochen Mehmood, Adnan Raivio, Leevi Huttunen, Heikki Raumonen, Pasi Virkki, Johanna |
author_sort | He, Han |
collection | PubMed |
description | This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform surface creates an electrical connection from specific ICs to the antenna, which enables the connected ICs to be read with an external UHF (ultra-haigh frequency) RFID reader. In this paper, the platform is demonstrated to recognize handwritten numbers 0–9. The raw data collected by the platform are a sequence of IDs from the touched ICs. The system converts the data into bitmaps and their details are increased by interpolating between neighboring samples using the sequential information of IDs. These images of digits written on the platform can be classified, with enough accuracy for practical use, by deep learning. The recognition system was trained and tested with samples from six volunteers using the platform. The real-time number recognition ability of the ClothFace technology is demonstrated to work successfully with a very low error rate. The overall recognition accuracy of the platform is 94.6% and the accuracy for each digit is between 91.1% and 98.3%. As the solution is fully passive and gets all the needed energy from the external RFID reader, it enables a maintenance-free and cost-effective user interface that can be integrated into clothing and into textiles around us. |
format | Online Article Text |
id | pubmed-7506965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75069652020-09-30 ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition He, Han Chen, Xiaochen Mehmood, Adnan Raivio, Leevi Huttunen, Heikki Raumonen, Pasi Virkki, Johanna Sensors (Basel) Communication This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform surface creates an electrical connection from specific ICs to the antenna, which enables the connected ICs to be read with an external UHF (ultra-haigh frequency) RFID reader. In this paper, the platform is demonstrated to recognize handwritten numbers 0–9. The raw data collected by the platform are a sequence of IDs from the touched ICs. The system converts the data into bitmaps and their details are increased by interpolating between neighboring samples using the sequential information of IDs. These images of digits written on the platform can be classified, with enough accuracy for practical use, by deep learning. The recognition system was trained and tested with samples from six volunteers using the platform. The real-time number recognition ability of the ClothFace technology is demonstrated to work successfully with a very low error rate. The overall recognition accuracy of the platform is 94.6% and the accuracy for each digit is between 91.1% and 98.3%. As the solution is fully passive and gets all the needed energy from the external RFID reader, it enables a maintenance-free and cost-effective user interface that can be integrated into clothing and into textiles around us. MDPI 2020-08-28 /pmc/articles/PMC7506965/ /pubmed/32872287 http://dx.doi.org/10.3390/s20174878 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication He, Han Chen, Xiaochen Mehmood, Adnan Raivio, Leevi Huttunen, Heikki Raumonen, Pasi Virkki, Johanna ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition |
title | ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition |
title_full | ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition |
title_fullStr | ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition |
title_full_unstemmed | ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition |
title_short | ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition |
title_sort | clothface: a batteryless rfid-based textile platform for handwriting recognition |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506965/ https://www.ncbi.nlm.nih.gov/pubmed/32872287 http://dx.doi.org/10.3390/s20174878 |
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