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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10377343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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