ArticleBioengineering (Basel, Switzerland)2023
GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Lighting effects on optimal facial regions for remote heart rate measurement.NPJ cardiovascular health · 2026Article
- Roadmap of remote photoplethysmography from heart rate measurement toward clinical translation.NPJ digital medicine · 2026Article
- DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation.Frontiers in physiology · 2026Article
- The role of face regions in remote photoplethysmography for contactless heart rate monitoring.NPJ digital medicine · 2025Article
- A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning.Biomedical engineering online · 2025Review
- LieRHRV system for remote lie detection using heart rate variability parameters.Scientific reports · 2024Article
- Optimal facial regions for remote heart rate measurement during physical and cognitive activities.NPJ cardiovascular health · 2024Article
- A machine learning-based approach for constructing remote photoplethysmogram signals from video cameras.Communications medicine · 2024Article
- Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.Frontiers in bioengineering and biotechnology · 2024Review
- Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG.Frontiers in physiology · 2023Article
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Authors and funding
3 authors.
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Abstract
Remote photoplethysmography (rPPG) is a promising contactless technology that uses videos of faces to extract health parameters, such as heart rate. Several methods for transforming red, green, and blue (RGB) video signals into rPPG signals have been introduced in the existing literature. The RGB signals represent variations in the reflected luminance from the skin surface of an individual over a given period of time. These methods attempt to find the best combination of color channels to reconstruct an rPPG signal. Usually, rPPG methods use a combination of prepossessed color channels to convert the three RGB signals to one rPPG signal that is most influenced by blood volume changes. This study examined simple yet effective methods to convert the RGB to rPPG, relying only on RGB signals without applying complex mathematical models or machine learning algorithms. A new method, GRGB rPPG, was proposed that outperformed most machine-learning-based rPPG methods and was robust to indoor lighting and participant motion. Moreover, the proposed method estimated the heart rate better than well-established rPPG methods. This paper also discusses the results and provides recommendations for further research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.