The measurement of the heart rate (HR) is of vitalimportance in modern medicine. Advancements in medical technology have resulted in a myriad of techniques to measure and analyze these bio-signals, and the advent of telemedicine and the post-COVID-19 world has placed greater emphasis on contact-free measurement tools.

Previous works have explored several methods of contact-free heart rate measurement. Remote Photo-Plethysmography(rPPG) measures HR from RGB camera streams by detecting and analyzing the frequency of “micro-blushes” in the skin corresponding to pulse; this method can also be used to estimate SpO2. Eulerian video magnification instead attempts to detectsubtle micro-movements caused by the pulse to measure HR aswell as RR. Ballistocardiography tracks longitudinal movements of feature points to estimate HR. However, there is little research on applying these (typically computationally-intensive) algorithms in the context of real-time, low-performance embedded systems.

This paper evaluates the computational requirements of algorithms used in extracting bio-signals using an RGB camera. It focuses on rPPG, a camera based method for extracting heart rate from video. Several rPPG implementations are tested on standard hardware and benchmarked on real-time performance and memory. The experiments were conducted on publicly available datasets. Additionally, region-of-interest selection algorithms are also compared. These results are of much use in developing embedded devices for remote monitoring of bio-signals and provide some insight into algorithms viable for use in real-time contexts.