Conventional and deep learning methods in heart rate estimation from RGB face videos

Contactless Vital Signs Monitoring is a fast advancing scientific field aims to employ monitoring methods that do not necessitate the use of leads or physical attachments to the patient in order to overcome the shortcomings and limits of traditional monitoring systems. Several traditional methods ha...

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Bibliographic Details
Published in:Physiological measurement 2024-02, Vol.45 (2)
Main Authors: Helwan, Abdulkader, Azar, Danielle, Ma’aitah, Mohamad Khaleel Sallam
Format: Article
Language:English
Subjects:
CNN
deep learning
heart rate
rPPG
vital signs
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Summary:Contactless Vital Signs Monitoring is a fast advancing scientific field aims to employ monitoring methods that do not necessitate the use of leads or physical attachments to the patient in order to overcome the shortcomings and limits of traditional monitoring systems. Several traditional methods have been applied to extract the heart rate (HR) signal from the face. Moreover, machine learning has recently contributed majorly to the development of such field in which deep networks and other deep learning methods were employed to extract the heart rate signal from RGB face videos. In this paper, we evaluate the state-of-the-art conventional and deep learning methods for heart rate estimate, focusing on the limits of deep learning methods and the availability of less-controlled face video datasets. We aim to present an extensive review that helps understanding the various approaches of remote Photoplethysmography (rPPG) extraction and HR estimation in addition to their drawbacks and benefits.
ISSN:0967-3334
1361-6579
DOI:10.1088/1361-6579/ad1458