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An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts

The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can b...

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Autores principales: Vicente-Samper, José María, Tamantini, Christian, Ávila-Navarro, Ernesto, De La Casa-Lillo, Miguel Ángel, Zollo, Loredana, Sabater-Navarro, José María, Cordella, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377343/
https://www.ncbi.nlm.nih.gov/pubmed/37504116
http://dx.doi.org/10.3390/bios13070718
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author Vicente-Samper, José María
Tamantini, Christian
Ávila-Navarro, Ernesto
De La Casa-Lillo, Miguel Ángel
Zollo, Loredana
Sabater-Navarro, José María
Cordella, Francesca
author_facet Vicente-Samper, José María
Tamantini, Christian
Ávila-Navarro, Ernesto
De La Casa-Lillo, Miguel Ángel
Zollo, Loredana
Sabater-Navarro, José María
Cordella, Francesca
author_sort Vicente-Samper, José María
collection PubMed
description The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.
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spelling pubmed-103773432023-07-29 An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts Vicente-Samper, José María Tamantini, Christian Ávila-Navarro, Ernesto De La Casa-Lillo, Miguel Ángel Zollo, Loredana Sabater-Navarro, José María Cordella, Francesca Biosensors (Basel) Article The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG. MDPI 2023-07-07 /pmc/articles/PMC10377343/ /pubmed/37504116 http://dx.doi.org/10.3390/bios13070718 Text en © 2023 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
Vicente-Samper, José María
Tamantini, Christian
Ávila-Navarro, Ernesto
De La Casa-Lillo, Miguel Ángel
Zollo, Loredana
Sabater-Navarro, José María
Cordella, Francesca
An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts
title An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts
title_full An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts
title_fullStr An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts
title_full_unstemmed An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts
title_short An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts
title_sort ml-based approach to reconstruct heart rate from ppg in presence of motion artifacts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377343/
https://www.ncbi.nlm.nih.gov/pubmed/37504116
http://dx.doi.org/10.3390/bios13070718
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