Intelligent Telemedicine Systems for Contactless Heart Rate Estimation using Deep Learning-based rPPG

Authors

• Do Duc Thinh, University of Transport and Communications, Vietnam
• Nguyen Duc Manh, University of Transport and Communications, Vietnam
• Nguyen Thi Bich Ngoc, Sao Do University, Vietnam
• Vu Van Huan, Hanoi University of Natural Resources and Environment, Vietnam

Corresponding Author ORCID Identifer

Vu Van Huan https://orcid.org/0009-0006-6198-480X

Section

(Thematic) AI in Health and Wellness

Abstract

Over the last ten years, telemedicine has undergone significant developments, from simple communication-based healthcare to smart and data-driven solutions. The solutions are now powered by Artificial Intelligence (AI), Machine Learning (ML), and Internet of Medical Things (IoMT) technologies. Physiological monitoring is traditionally done with contact-based sensors. The sensors include Electrocardiogram (ECG) and pulse oximeters. The sensors have various disadvantages, including hardware costs, difficulty in continuous usage, and discomfort for patients. Photoplethysmography (rPPG), a remote method of physiological monitoring, is a breakthrough technology. The method is used to estimate physiological signals, including Heart Rate (HR), from video streams captured by standard RGB cameras. The paper aims to explore spatial-temporal deep learning frameworks for remote rPPG as part of a standard five-layer telemedicine architecture. We explain a general ML pipeline, along with the benefits of decomposing the spatial and temporal features of images and motion to improve signal extraction against environmental noise. Besides, the paper presents some of the deployment issues, which include motion effects, lighting, as well as bias in the algorithms, specifically with respect to melanin absorption in human skin. The research also presents some of the avenues of future research, specifically with respect to model compression, which will help move from a cloud to an edge device, thus helping to improve the privacy of users.

Cite This Article

Thinh, D. D., Manh, N. D., Ngoc, N. T. B., & Huan, V. V. (2026). Intelligent Telemedicine Systems for Contactless Heart Rate Estimation using Deep Learning-based rPPG. Journal of Informatics and Web Engineering, 5(2), 405–423. https://doi.org/10.33093/jiwe.2026.5.2.24

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