TY - GEN
T1 - Remote photoplethysmography (rPPG) for contactless heart rate monitoring using a single monochrome and color camera
AU - Ma, Xiaocong
AU - Tobón, Diana P.
AU - El Saddik, Abdulmotaleb
PY - 2020
Y1 - 2020
N2 - Human vital signs are essential information that are closely related to both physical cardiac assessments and psychological emotion studies. One of the most important data is the heart rate, which is closely connected to the clinical state of the human body. Modern image processing technologies, such as Remote Photoplethysmography (rPPG), have enabled us to collect and extract the heart rate data from the body by just using an optical sensor and not making any physical contact. In this paper, we propose a real-time camera-based heart rate detector system using computer vision and signal processing techniques. The software of the system is designed to be compatible with both an ordinary built-in color webcam and an industry grade grayscale camera. In addition, we conduct an analysis based on the experimental results collected from a combination of test subjects varying in genders, races, and ages, followed by a quick performance comparison between the color webcam and an industry grayscale camera. The final calculations on percentage error have shown interesting results as the built-in color webcam with the digital spatial filter and the grayscale camera with optical filter achieved relatively similar accuracy under both still and exercising conditions. However, the correlation calculations, on the other hand, have shown that compared to the webcam, the industry grade camera is superior in stability when facial artifacts are presented.
AB - Human vital signs are essential information that are closely related to both physical cardiac assessments and psychological emotion studies. One of the most important data is the heart rate, which is closely connected to the clinical state of the human body. Modern image processing technologies, such as Remote Photoplethysmography (rPPG), have enabled us to collect and extract the heart rate data from the body by just using an optical sensor and not making any physical contact. In this paper, we propose a real-time camera-based heart rate detector system using computer vision and signal processing techniques. The software of the system is designed to be compatible with both an ordinary built-in color webcam and an industry grade grayscale camera. In addition, we conduct an analysis based on the experimental results collected from a combination of test subjects varying in genders, races, and ages, followed by a quick performance comparison between the color webcam and an industry grayscale camera. The final calculations on percentage error have shown interesting results as the built-in color webcam with the digital spatial filter and the grayscale camera with optical filter achieved relatively similar accuracy under both still and exercising conditions. However, the correlation calculations, on the other hand, have shown that compared to the webcam, the industry grade camera is superior in stability when facial artifacts are presented.
KW - Computer vision
KW - Heart rate
KW - Photoplethysmography
KW - rPPG
KW - Signal processing
UR - http://www.scopus.com/inward/record.url?scp=85089607797&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-54407-2_21
DO - 10.1007/978-3-030-54407-2_21
M3 - Contribución a la conferencia
AN - SCOPUS:85089607797
SN - 9783030544065
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 248
EP - 262
BT - Smart Multimedia - 2nd International Conference, ICSM 2019, Revised Selected Papers
A2 - McDaniel, Troy
A2 - Berretti, Stefano
A2 - Curcio, Igor D.D.
A2 - Basu, Anup
PB - Springer
Y2 - 16 December 2019 through 18 December 2019
ER -