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Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into...

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Autores principales: Silva, Aline Santos, Correia, Miguel Velhote, de Melo, Francisco, da Silva, Hugo Plácido
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185406/
https://www.ncbi.nlm.nih.gov/pubmed/35684820
http://dx.doi.org/10.3390/s22114201
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author Silva, Aline Santos
Correia, Miguel Velhote
de Melo, Francisco
da Silva, Hugo Plácido
author_facet Silva, Aline Santos
Correia, Miguel Velhote
de Melo, Francisco
da Silva, Hugo Plácido
author_sort Silva, Aline Santos
collection PubMed
description This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24–70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 ± 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 ± 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.
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spelling pubmed-91854062022-06-11 Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography Silva, Aline Santos Correia, Miguel Velhote de Melo, Francisco da Silva, Hugo Plácido Sensors (Basel) Article This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24–70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 ± 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 ± 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals. MDPI 2022-05-31 /pmc/articles/PMC9185406/ /pubmed/35684820 http://dx.doi.org/10.3390/s22114201 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Silva, Aline Santos
Correia, Miguel Velhote
de Melo, Francisco
da Silva, Hugo Plácido
Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography
title Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography
title_full Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography
title_fullStr Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography
title_full_unstemmed Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography
title_short Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography
title_sort identity recognition in sanitary facilities using invisible electrocardiography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185406/
https://www.ncbi.nlm.nih.gov/pubmed/35684820
http://dx.doi.org/10.3390/s22114201
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