Article
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IEEE Journal of Biomedical and Health Informatics
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publication venue for: Non-Contact HR Monitoring via Smartphone and Webcam during Different Respiratory Maneuvers and Body Movements

Generically Dependent Continuant
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Non-Contact HR Monitoring via Smartphone and Webcam during Different Respiratory Maneuvers and Body Movements
freetext keyword: biomedical monit-oring; Breathing maneuvers; heart rate and variability; imaging photoplet-hysmography Cameras; Electrocardiography; Smartphones; Video recording; Automatic face detection; Body movements; Healthiness condition; Imaging photoplethysmography (IPPG); Measurement methods; Monitoring techniques; Motion conditions; Video sequences; Face recognition; adult; Article; body movement; breathing; breathing rate; detection algorithm; electrocardiography; female; gold standard; heart rate; human; human experiment; male; mathematical parameters; normal human; oximetry; photoelectric plethysmography; skin; videorecording; algorithm; heart rate; physiologic monitoring; smartphone; Algorithms; Heart Rate; Humans; Monitoring, Physiologic; Photoplethysmography; Smartphone
abstract: As a reliable indicator for individual's healthiness conditions, heart rate (HR) has been widely considered and used. Imaging photoplethysmography (iPPG) is recently highlighted as a promising HR measurement method, due to its non-contact characteristics, by extracting the HR from facial video recordings. In this study, we propose a camera-based HR monitoring technique that estimates HR information from iPPG signals extracted from a video sequence. Videos were recorded using a smartphone or a laptop camera. We adopted the plane-orthogonal-to-skin (POS) method to compute iPPG. The proposed method is evaluated by applying it to extract HR of 9 subjects at rest and during two motion conditions (lateral and frontal) while they were performing several respiratory maneuvers-spontaneous, metronome, and forced. Automatic face detection algorithms were implemented in the proposed method. Our experimental results show that mean values of HR have 0.56%25 error and 99.4%25 accuracy when compared to HR calculated from the gold-standard electrocardiography (ECG) reference in diverse conditions of motions and respiratory maneuvers. © 2013 IEEE.
page end: 612
related by: authorship_63656
issue: 2
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pmid: 32750916
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has publication venue: IEEE Journal of Biomedical and Health Informatics
artnum: 9103223
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doi: 10.1109/JBHI.2020.2998399
manufactures: Biopac, United States; Hewlett Packard, United States; Mathworks, United States
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crisid: 8926
abstract: As a reliable indicator for individual%27s healthiness conditions, heart rate (HR) has been widely considered and used. Imaging photoplethysmography (iPPG) is recently highlighted as a promising HR measurement method, due to its non-contact characteristics, by extracting the HR from facial video recordings. In this study, we propose a camera-based HR monitoring technique that estimates HR information from iPPG signals extracted from a video sequence. Videos were recorded using a smartphone or a laptop camera. We adopted the plane-orthogonal-to-skin (POS) method to compute iPPG. The proposed method is evaluated by applying it to extract HR of 9 subjects at rest and during two motion conditions (lateral and frontal) while they were performing several respiratory maneuvers-spontaneous, metronome, and forced. Automatic face detection algorithms were implemented in the proposed method. Our experimental results show that mean values of HR have 0.56%25 error and 99.4%25 accuracy when compared to HR calculated from the gold-standard electrocardiography (ECG) reference in diverse conditions of motions and respiratory maneuvers. © 2013 IEEE.
type: Generically Dependent Continuant
date time value: article date 8926
tradenames: avilion dm4, Hewlett Packard, United States; Biopac MP150, Biopac, United States; MATLAB 2017, Mathworks, United States
estado: published
origen: scopus
volume: 25
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date: 2026-08-27T06:29:04
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