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ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition

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ClothFace : A Batteryless RFID-Based Textile Platform for Handwriting Recognition. / He, Han; Chen, Xiaochen; Mehmood, Adnan; Raivio, Leevi; Huttunen, Heikki; Raumonen, Pasi; Virkki, Johanna.

julkaisussa: Sensors (Basel, Switzerland), Vuosikerta 20, Nro 17, 4878, 28.08.2020.

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He, Han ; Chen, Xiaochen ; Mehmood, Adnan ; Raivio, Leevi ; Huttunen, Heikki ; Raumonen, Pasi ; Virkki, Johanna. / ClothFace : A Batteryless RFID-Based Textile Platform for Handwriting Recognition. Julkaisussa: Sensors (Basel, Switzerland). 2020 ; Vuosikerta 20, Nro 17.

Bibtex - Lataa

@article{1f65f5963a6745178ad1c943cf68c548,
title = "ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition",
abstract = "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.",
keywords = "deep learning, human–machine interaction, passive UHF RFID, textile electronics, user interface, wearables",
author = "Han He and Xiaochen Chen and Adnan Mehmood and Leevi Raivio and Heikki Huttunen and Pasi Raumonen and Johanna Virkki",
year = "2020",
month = "8",
day = "28",
doi = "10.3390/s20174878",
language = "English",
volume = "20",
journal = "Sensors",
issn = "1424-8220",
publisher = "MDPI",
number = "17",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - ClothFace

T2 - A Batteryless RFID-Based Textile Platform for Handwriting Recognition

AU - He, Han

AU - Chen, Xiaochen

AU - Mehmood, Adnan

AU - Raivio, Leevi

AU - Huttunen, Heikki

AU - Raumonen, Pasi

AU - Virkki, Johanna

PY - 2020/8/28

Y1 - 2020/8/28

N2 - 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.

AB - 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.

KW - deep learning

KW - human–machine interaction

KW - passive UHF RFID

KW - textile electronics

KW - user interface

KW - wearables

U2 - 10.3390/s20174878

DO - 10.3390/s20174878

M3 - Article

VL - 20

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 17

M1 - 4878

ER -