An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 7; p. 3387
• Main Authors: Boccignone, Giuseppe, D’Amelio, Alessandro, Ghezzi, Omar, Grossi, Giuliano, Lanzarotti, Raffaella
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.03.2023; MDPI
• Subjects: Algorithms; Blood; Cameras; Datasets; Deep learning; empirical mode decomposition; Heart rate; Heart Rate - physiology; Humans; incremental merge segmentation; Morphology; Photoplethysmography - methods; Pulse oximetry; pyVHR; remote photoplethysmography; remote respiratory rate estimation; Respiration; Respiratory Rate - physiology; Sensors; Signal processing; Signal Processing, Computer-Assisted; singular spectrum analysis; Smartphones; Spectrum analysis; remote respiratory rate estimation; empirical mode decomposition; pyVHR; contactless respiration monitoring; remote photoplethysmography; incremental merge segmentation; vital signs from video; singular spectrum analysis

The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Rece...