GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation

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.

Optimal signal quality index for remote photoplethysmogram sensing

“…Its applications span from clinical patient monitoring to consumer health, showcasing its versatility. Recent research in rPPG has explored the evaluation of red, green, and blue channels for heart rate detection 3 , the development of less complex methods for improved heart rate measurement via rPPG 4 , the evaluation of biases in rPPG methods 5 , and investigations into the effectiveness of various rPPG methods in different settings 6,7 . Additionally, there have been studies on the use of machine learning for blood pressure detection using rPPG 8,9 .…”

Evaluating the Influence of Room Illumination on Camera-Based Physiological Measurements for the Assessment of Screen-Based Media

“…Despite the algorithm showing poor performance within this difficult application area, it has become one of the most popular approaches for rPPG. Notably, Haugg et al showed a drop in performance from an average error of 1.91 beats per minute (BPM) with the subject resting to 14.81 BPM with the subject using the gym [33]. The algorithm adds sophistication to previously proposed methods based on blind source separation, by considering the difference between light reflected off the surface of the skin and light which has travelled through the skin and therefore contains information associated with the subject's cardiac cycle.…”

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG

“…While the green channel's potential is acknowledged, the roles of blue and red channels, assessed under limited conditions, remain unclear. Our study addresses these limitations, utilizing three diverse datasets with varying camera types, pulse oximeters, lighting conditions, distances, and participant activities (Frey et al, 2022;Haugg et al, 2023).…”