Article
Document
Information Content Entity
IEEE Journal of Biomedical and Health Informatics
Generically Dependent Continuant
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
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.
Non-Contact HR Monitoring via Smartphone and Webcam during Different Respiratory Maneuvers and Body Movements
Biopac, United States; Hewlett Packard, United States; Mathworks, United States
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.
avilion dm4, Hewlett Packard, United States; Biopac MP150, Biopac, United States; MATLAB 2017, Mathworks, United States
RDF description of Non-Contact HR Monitoring via Smartphone and Webcam during Different Respiratory Maneuvers and Body Movements - http://orbis.uaslp.mx/vivo/individual/scopus8925