Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition

Abstract The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and...

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Autores principales:	Giulio Rosati, Giulia Cisotto, Daniele Sili, Luca Compagnucci, Chiara De Giorgi, Enea Francesco Pavone, Alessandro Paccagnella, Viviana Betti
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5685ce0c229f4b838db6f2c53e57ae04
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spelling	oai:doaj.org-article:5685ce0c229f4b838db6f2c53e57ae042021-12-02T16:17:28ZInkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition10.1038/s41598-021-94526-52045-2322https://doaj.org/article/5685ce0c229f4b838db6f2c53e57ae042021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94526-5https://doaj.org/toc/2045-2322Abstract The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93–95% for flexion and extension, respectively.Giulio RosatiGiulia CisottoDaniele SiliLuca CompagnucciChiara De GiorgiEnea Francesco PavoneAlessandro PaccagnellaViviana BettiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Giulio Rosati Giulia Cisotto Daniele Sili Luca Compagnucci Chiara De Giorgi Enea Francesco Pavone Alessandro Paccagnella Viviana Betti Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition
description	Abstract The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93–95% for flexion and extension, respectively.
format	article
author	Giulio Rosati Giulia Cisotto Daniele Sili Luca Compagnucci Chiara De Giorgi Enea Francesco Pavone Alessandro Paccagnella Viviana Betti
author_facet	Giulio Rosati Giulia Cisotto Daniele Sili Luca Compagnucci Chiara De Giorgi Enea Francesco Pavone Alessandro Paccagnella Viviana Betti
author_sort	Giulio Rosati
title	Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition
title_short	Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition
title_full	Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition
title_fullStr	Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition
title_full_unstemmed	Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition
title_sort	inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5685ce0c229f4b838db6f2c53e57ae04
work_keys_str_mv	AT giuliorosati inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition AT giuliacisotto inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition AT danielesili inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition AT lucacompagnucci inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition AT chiaradegiorgi inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition AT eneafrancescopavone inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition AT alessandropaccagnella inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition AT vivianabetti inkjetprintedfullycustomizableandlowcostelectrodesmatrixforgesturerecognition
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Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition

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