pyVHR: a Python framework for remote photoplethysmography

Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability t...

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Vydané v:	PeerJ. Computer science Ročník 8; s. e929
Hlavní autori:	Boccignone, Giuseppe, Conte, Donatello, Cuculo, Vittorio, D’Amelio, Alessandro, Grossi, Giuliano, Lanzarotti, Raffaella, Mortara, Edoardo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States PeerJ, Inc 15.04.2022 PeerJ PeerJ Inc
Predmet:	Blood volume Case studies Cognitive science Computer Science Computer Vision Computer Vision and Pattern Recognition Contactless monitoring Datasets Deep rPPG Deepfake Detection Heart rate Heart Rate Estimation Human-Computer Interaction Image Processing Learning Pipelining (computers) Python Remote photoplethysmography Signal and Image Processing Signal processing Statistical analysis Video Deepfake Detection Remote photoplethysmography Contactless monitoring Deep rPPG Heart Rate Estimation Computer Vision Remote photoplethysmography Subjects Human-Computer Interaction
ISSN:	2376-5992, 2376-5992
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Abstract	Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability to estimate the blood volume pulse (BVP) signal upon which BPMs (Beats per Minute) can be estimated. Furthermore, learning-based rPPG methods have been recently proposed. The present pyVHR framework represents a multi-stage pipeline covering the whole process for extracting and analyzing HR fluctuations. It is designed for both theoretical studies and practical applications in contexts where wearable sensors are inconvenient to use. Namely, pyVHR supports either the development, assessment and statistical analysis of novel rPPG methods, either traditional or learning-based, or simply the sound comparison of well-established methods on multiple datasets. It is built up on accelerated Python libraries for video and signal processing as well as equipped with parallel/accelerated ad-hoc procedures paving the way to online processing on a GPU. The whole accelerated process can be safely run in real-time for 30 fps HD videos with an average speedup of around 5. This paper is shaped in the form of a gentle tutorial presentation of the framework.
AbstractList	Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability to estimate the blood volume pulse (BVP) signal upon which BPMs (Beats per Minute) can be estimated. Furthermore, learning-based rPPG methods have been recently proposed. The present pyVHR framework represents a multi-stage pipeline covering the whole process for extracting and analyzing HR fluctuations. It is designed for both theoretical studies and practical applications in contexts where wearable sensors are inconvenient to use. Namely, pyVHR supports either the development, assessment and statistical analysis of novel rPPG methods, either traditional or learning-based, or simply the sound comparison of well-established methods on multiple datasets. It is built up on accelerated Python libraries for video and signal processing as well as equipped with parallel/accelerated ad-hoc procedures paving the way to online processing on a GPU. The whole accelerated process can be safely run in real-time for 30 fps HD videos with an average speedup of around 5. This paper is shaped in the form of a gentle tutorial presentation of the framework. Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability to estimate the blood volume pulse (BVP) signal upon which BPMs (Beats per Minute) can be estimated. Furthermore, learning-based rPPG methods have been recently proposed. The present pyVHR framework represents a multi-stage pipeline covering the whole process for extracting and analyzing HR fluctuations. It is designed for both theoretical studies and practical applications in contexts where wearable sensors are inconvenient to use. Namely, pyVHR supports either the development, assessment and statistical analysis of novel rPPG methods, either traditional or learning-based, or simply the sound comparison of well-established methods on multiple datasets. It is built up on accelerated Python libraries for video and signal processing as well as equipped with parallel/accelerated ad-hoc procedures paving the way to online processing on a GPU. The whole accelerated process can be safely run in real-time for 30 fps HD videos with an average speedup of around 5. This paper is shaped in the form of a gentle tutorial presentation of the framework.Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability to estimate the blood volume pulse (BVP) signal upon which BPMs (Beats per Minute) can be estimated. Furthermore, learning-based rPPG methods have been recently proposed. The present pyVHR framework represents a multi-stage pipeline covering the whole process for extracting and analyzing HR fluctuations. It is designed for both theoretical studies and practical applications in contexts where wearable sensors are inconvenient to use. Namely, pyVHR supports either the development, assessment and statistical analysis of novel rPPG methods, either traditional or learning-based, or simply the sound comparison of well-established methods on multiple datasets. It is built up on accelerated Python libraries for video and signal processing as well as equipped with parallel/accelerated ad-hoc procedures paving the way to online processing on a GPU. The whole accelerated process can be safely run in real-time for 30 fps HD videos with an average speedup of around 5. This paper is shaped in the form of a gentle tutorial presentation of the framework.
ArticleNumber	e929
Author	Boccignone, Giuseppe D’Amelio, Alessandro Cuculo, Vittorio Grossi, Giuliano Mortara, Edoardo Conte, Donatello Lanzarotti, Raffaella
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Copyright	2022 Boccignone et al. 2022 Boccignone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. licence_http://creativecommons.org/publicdomain/zero 2022 Boccignone et al. 2022 Boccignone et al.
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Keywords	Deepfake Detection Remote photoplethysmography Contactless monitoring Deep rPPG Heart Rate Estimation Computer Vision Remote photoplethysmography Subjects Human-Computer Interaction
Language	English
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Snippet	Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective...
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SubjectTerms	Blood volume Case studies Cognitive science Computer Science Computer Vision Computer Vision and Pattern Recognition Contactless monitoring Datasets Deep rPPG Deepfake Detection Heart rate Heart Rate Estimation Human-Computer Interaction Image Processing Learning Pipelining (computers) Python Remote photoplethysmography Signal and Image Processing Signal processing Statistical analysis Video
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Title	pyVHR: a Python framework for remote photoplethysmography
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