Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank

Remote photoplethysmography (rPPG) is a non-contact technology that monitors heart activity by detecting subtle color changes within the facial blood vessels. It provides an unconstrained and unconscious approach that can be widely applied to health monitoring systems. In recent years, research has been actively conducted to improve rPPG signals and to extract significant information from facial videos. However, rPPG can be vulnerable to degradation due to changes in the illumination and motion of a subject, and overcoming these challenges remains difficult. In this study, we propose a machine learning-based filter bank (MLFB) noise reduction algorithm to improve the quality of rPPG signals. The MLFB algorithm determines the optimal spectral band for extracting information on cardiovascular activity and reconstructing an rPPG signal using a support vector machine. The proposed approach was validated with an open dataset, achieving a 35.5% (i.e., resulting in a mean absolute error of 2.5 beats per minute) higher accuracy than those of conventional methods. The proposed algorithm can be integrated into various rPPG algorithms for the pre-processing of RGB signals. Moreover, its computational efficiency is expected to enable straightforward implementation in system development, making it broadly applicable across the healthcare field.