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Digital electronics in fibres enable fabric-based machine-learning inference

Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-t...

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Autores principales: Loke, Gabriel, Khudiyev, Tural, Wang, Brian, Fu, Stephanie, Payra, Syamantak, Shaoul, Yorai, Fung, Johnny, Chatziveroglou, Ioannis, Chou, Pin-Wen, Chinn, Itamar, Yan, Wei, Gitelson-Kahn, Anna, Joannopoulos, John, Fink, Yoel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175338/
https://www.ncbi.nlm.nih.gov/pubmed/34083521
http://dx.doi.org/10.1038/s41467-021-23628-5
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author Loke, Gabriel
Khudiyev, Tural
Wang, Brian
Fu, Stephanie
Payra, Syamantak
Shaoul, Yorai
Fung, Johnny
Chatziveroglou, Ioannis
Chou, Pin-Wen
Chinn, Itamar
Yan, Wei
Gitelson-Kahn, Anna
Joannopoulos, John
Fink, Yoel
author_facet Loke, Gabriel
Khudiyev, Tural
Wang, Brian
Fu, Stephanie
Payra, Syamantak
Shaoul, Yorai
Fung, Johnny
Chatziveroglou, Ioannis
Chou, Pin-Wen
Chinn, Itamar
Yan, Wei
Gitelson-Kahn, Anna
Joannopoulos, John
Fink, Yoel
author_sort Loke, Gabriel
collection PubMed
description Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-to-fibre approach is used to produce tens of metres of flexible fibre containing hundreds of interspersed, digital temperature sensors and memory devices with a memory density of ~7.6 × 10(5) bits per metre. The entire ensemble of devices are individually addressable and independently operated through a single connection at the fibre edge, overcoming the perennial single-fibre single-device limitation and increasing system reliability. The digital fibre, when incorporated within a shirt, collects and stores body temperature data over multiple days, and enables real-time inference of wearer activity with an accuracy of 96% through a trained neural network with 1650 neuronal connections stored within the fibre. The ability to realise digital devices within a fibre strand which can not only measure and store physiological parameters, but also harbour the neural networks required to infer sensory data, presents intriguing opportunities for worn fabrics that sense, memorise, learn, and infer situational context.
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spelling pubmed-81753382021-06-07 Digital electronics in fibres enable fabric-based machine-learning inference Loke, Gabriel Khudiyev, Tural Wang, Brian Fu, Stephanie Payra, Syamantak Shaoul, Yorai Fung, Johnny Chatziveroglou, Ioannis Chou, Pin-Wen Chinn, Itamar Yan, Wei Gitelson-Kahn, Anna Joannopoulos, John Fink, Yoel Nat Commun Article Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-to-fibre approach is used to produce tens of metres of flexible fibre containing hundreds of interspersed, digital temperature sensors and memory devices with a memory density of ~7.6 × 10(5) bits per metre. The entire ensemble of devices are individually addressable and independently operated through a single connection at the fibre edge, overcoming the perennial single-fibre single-device limitation and increasing system reliability. The digital fibre, when incorporated within a shirt, collects and stores body temperature data over multiple days, and enables real-time inference of wearer activity with an accuracy of 96% through a trained neural network with 1650 neuronal connections stored within the fibre. The ability to realise digital devices within a fibre strand which can not only measure and store physiological parameters, but also harbour the neural networks required to infer sensory data, presents intriguing opportunities for worn fabrics that sense, memorise, learn, and infer situational context. Nature Publishing Group UK 2021-06-03 /pmc/articles/PMC8175338/ /pubmed/34083521 http://dx.doi.org/10.1038/s41467-021-23628-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Loke, Gabriel
Khudiyev, Tural
Wang, Brian
Fu, Stephanie
Payra, Syamantak
Shaoul, Yorai
Fung, Johnny
Chatziveroglou, Ioannis
Chou, Pin-Wen
Chinn, Itamar
Yan, Wei
Gitelson-Kahn, Anna
Joannopoulos, John
Fink, Yoel
Digital electronics in fibres enable fabric-based machine-learning inference
title Digital electronics in fibres enable fabric-based machine-learning inference
title_full Digital electronics in fibres enable fabric-based machine-learning inference
title_fullStr Digital electronics in fibres enable fabric-based machine-learning inference
title_full_unstemmed Digital electronics in fibres enable fabric-based machine-learning inference
title_short Digital electronics in fibres enable fabric-based machine-learning inference
title_sort digital electronics in fibres enable fabric-based machine-learning inference
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175338/
https://www.ncbi.nlm.nih.gov/pubmed/34083521
http://dx.doi.org/10.1038/s41467-021-23628-5
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