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GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation
Remote photoplethysmography (rPPG) is a promising contactless technology that uses videos of faces to extract health parameters, such as heart rate. Several methods for transforming red, green, and blue (RGB) video signals into rPPG signals have been introduced in the existing literature. The RGB si...
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/PMC9952130/ https://www.ncbi.nlm.nih.gov/pubmed/36829737 http://dx.doi.org/10.3390/bioengineering10020243 |
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author | Haugg, Fridolin Elgendi, Mohamed Menon, Carlo |
author_facet | Haugg, Fridolin Elgendi, Mohamed Menon, Carlo |
author_sort | Haugg, Fridolin |
collection | PubMed |
description | Remote photoplethysmography (rPPG) is a promising contactless technology that uses videos of faces to extract health parameters, such as heart rate. Several methods for transforming red, green, and blue (RGB) video signals into rPPG signals have been introduced in the existing literature. The RGB signals represent variations in the reflected luminance from the skin surface of an individual over a given period of time. These methods attempt to find the best combination of color channels to reconstruct an rPPG signal. Usually, rPPG methods use a combination of prepossessed color channels to convert the three RGB signals to one rPPG signal that is most influenced by blood volume changes. This study examined simple yet effective methods to convert the RGB to rPPG, relying only on RGB signals without applying complex mathematical models or machine learning algorithms. A new method, GRGB rPPG, was proposed that outperformed most machine-learning-based rPPG methods and was robust to indoor lighting and participant motion. Moreover, the proposed method estimated the heart rate better than well-established rPPG methods. This paper also discusses the results and provides recommendations for further research. |
format | Online Article Text |
id | pubmed-9952130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99521302023-02-25 GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation Haugg, Fridolin Elgendi, Mohamed Menon, Carlo Bioengineering (Basel) Communication Remote photoplethysmography (rPPG) is a promising contactless technology that uses videos of faces to extract health parameters, such as heart rate. Several methods for transforming red, green, and blue (RGB) video signals into rPPG signals have been introduced in the existing literature. The RGB signals represent variations in the reflected luminance from the skin surface of an individual over a given period of time. These methods attempt to find the best combination of color channels to reconstruct an rPPG signal. Usually, rPPG methods use a combination of prepossessed color channels to convert the three RGB signals to one rPPG signal that is most influenced by blood volume changes. This study examined simple yet effective methods to convert the RGB to rPPG, relying only on RGB signals without applying complex mathematical models or machine learning algorithms. A new method, GRGB rPPG, was proposed that outperformed most machine-learning-based rPPG methods and was robust to indoor lighting and participant motion. Moreover, the proposed method estimated the heart rate better than well-established rPPG methods. This paper also discusses the results and provides recommendations for further research. MDPI 2023-02-12 /pmc/articles/PMC9952130/ /pubmed/36829737 http://dx.doi.org/10.3390/bioengineering10020243 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 | Communication Haugg, Fridolin Elgendi, Mohamed Menon, Carlo GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation |
title | GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation |
title_full | GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation |
title_fullStr | GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation |
title_full_unstemmed | GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation |
title_short | GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation |
title_sort | grgb rppg: an efficient low-complexity remote photoplethysmography-based algorithm for heart rate estimation |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9952130/ https://www.ncbi.nlm.nih.gov/pubmed/36829737 http://dx.doi.org/10.3390/bioengineering10020243 |
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