pyVHR: a Python framework for remote photoplethysmography

• Published in: PeerJ. Computer science Vol. 8; p. e929
• Main Authors: Boccignone, Giuseppe, Conte, Donatello, Cuculo, Vittorio, D’Amelio, Alessandro, Grossi, Giuliano, Lanzarotti, Raffaella, Mortara, Edoardo
• Format: Journal Article
• Language: English
• Published: United States PeerJ, Inc 15.04.2022; PeerJ; PeerJ Inc
• Subjects: 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
• DOI: 10.7717/peerj-cs.929

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.