Improved motion robustness of remote-PPG by using the blood volume pulse signature

TLDR

Remote photoplethysmography uses a color camera to detect minute optical absorption changes caused by blood volume variations in the skin, enabling contact‑free monitoring of the blood volume pulse. The study demonstrates that the distinct absorption spectra of arterial blood and skin produce a specific vector in normalized RGB space, which can be exploited to design an rPPG algorithm with superior motion robustness compared to recent blind‑source‑separation and earlier chrominance‑based methods. The authors determine the exact RGB vector for a given light spectrum and camera filter characteristics, then validate the resulting rPPG algorithm on six gym videos from four exercising subjects, confirming its superior motion robustness. The new algorithm achieves a 68 % correct pulse‑rate detection rate versus 60 % for the best prior method, with SNR improving from –5 dB to –4 dB, and maintains comparable accuracy to the best previous method across 117 stationary subjects while slightly reducing SNR from +8.4 dB to +7.6 dB, thereby promising broader application of rPPG.

Abstract

Remote photoplethysmography (rPPG) enables contact-free monitoring of the blood volume pulse using a color camera. Essentially, it detects the minute optical absorption changes caused by blood volume variations in the skin. In this paper, we show that the different absorption spectra of arterial blood and bloodless skin cause the variations to occur along a very specific vector in a normalized RGB-space. The exact vector can be determined for a given light spectrum and for given transfer characteristics of the optical filters in the camera. We show that this 'signature' can be used to design an rPPG algorithm with a much better motion robustness than the recent methods based on blind source separation, and even better than the chrominance-based methods we published earlier. Using six videos recorded in a gym, with four subjects exercising on a range of fitness devices, we confirm the superior motion robustness of our newly proposed rPPG methods. A simple peak detector in the frequency domain returns the correct pulse-rate for 68% of total measurements compared to 60% for the best previous method, while the SNR of the pulse-signal improves from - 5 dB to - 4 dB. For a large population of 117 stationary subjects we prove that the accuracy is comparable to the best previous method, although the SNR of the pulse-signal drops from + 8.4 dB to + 7.6 dB. We expect the improved motion robustness to significantly widen the application scope of the rPPG-technique.