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

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...