An Improved Approach to the Performance of Remote Photoplethysmography

Heart rate is an important metric for determining physical and mental health. In recent years, remote photoplethysmography (rPPG) has been widely used in characterizing physiological signals in human subjects. Currently, research on non-contact detection of heart rate mainly focuses on the capture and separation of spectral signals from video imagery. However, this method is very sensitive to the movement of the test subject and light intensity variation, and this results in motion artifacts which presents challenges in extracting accurate physiological signals such as heart rate. In this paper, an improved method for rPPG signal preprocessing is proposed. Based on the well known red green blue (RGB) color space, we segmented skin tone in different color spaces and extracted rPPG signals, after which we use a skin segmentation training model based on the luminance component, the blue-difference chroma components, and red-difference chroma components (YCbCr), as well as hue saturation intensity (HSI) color models. In the experimental verification section, we compare the robustness of the signal on different color spaces. In summary, we are experimentally verifying a better image pre-processing method based on real-time rPPG, which results in more precise measurements through the comparative analysis of skin segmentation and signal quality.