Non-contact high precision pulse-rate monitoring system for moving subjects in different motion states

Remote photoplethysmography (rPPG) enables contact-free monitoring of the pulse rate by using a color camera. The fundamental limitation is that motion artifacts and changes in ambient light conditions greatly affect the accuracy of pulse-rate monitoring. We propose use of a high-speed camera and a...

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Bibliographic Details
Published inMedical & biological engineering & computing Vol. 61; no. 10; pp. 2769 - 2783
Main Authors Zhang, Qing, Lin, Xingsen, Zhang, Yuxin, Liu, Qian, Cai, Fuhong
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2023
Springer Nature B.V
Subjects
Accuracy
Algorithms
Bioengineering
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Cameras
color
Computer Applications
Deep learning
Engineering
health services
heart rate
High speed cameras
Human Physiology
Imaging
Monitoring systems
Original Article
Personal health
Pulse rate
Radiology
reproduction
Signal processing
Signal to noise ratio
rPPG
Motion blur
Pulse-rate measurement
Pulse wave features
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Summary:Remote photoplethysmography (rPPG) enables contact-free monitoring of the pulse rate by using a color camera. The fundamental limitation is that motion artifacts and changes in ambient light conditions greatly affect the accuracy of pulse-rate monitoring. We propose use of a high-speed camera and a motion suppression algorithm with high computational efficiency. This system incorporates a number of major improvements including reproduction of pulse wave details, high-precision pulse-rate monitoring of moving subjects, and excellent scene scalability. A series of quantization methods were used to evaluate the effect of different frame rates and different algorithms in pulse-rate monitoring of moving subjects. The experimental results show that use of 180-fps video and a Plane-Orthogonal-to-Skin (POS) algorithm can produce high-precision pulse-rate monitoring results with mean absolute error can be less than 5 bpm and the relative accuracy reaching 94.5%. Thus, it has significant potential to improve personal health care and intelligent health monitoring. Graphical Abstract
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ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-023-02884-1