# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 1 Introduction  
Heart rate (HR) is a primary biomarker of human physical and mental health and provides information on cardiovascular activity [1]. Heart rate is typically estimated using electrocardiography (ECG) or contact photoplethysmography (cPPG) [2, 3]. ECG records the electrical activity of the heart. cPPG employs a wearable optical sensor, commonly placed on the fingertip and earlobe, to measure changes in blood volume and oxygen saturation levels within the microvascular bed. cPPG specifically measures oxygen saturation levels in blood vessels and computes heart rate. Remote photoplethysmography (rPPG) is an unconstrained and non-contact heart rate monitoring approach that detects subtle changes in facial color due to variations in blood volume within the facial blood vessels. By measuring non-contact facial videos, rPPG can estimate heart rate and other related cardiovascular parameters [4]. This approach has been used to monitor heart rate during sleeping, driving, and exercising, where a physical contact sensor is not appropriate. It is also employed to determine the authenticity of deepfakes [5, 6, 7, 8]. In recent years, rPPG research has focused on extracting pulse signals from facial videos [9, 10]. [PERSON] et al. (2014) suggested the PBV (i.e., blood volume pulse signature method) for better motion robustness [11]. CHROM (i.e., chrominance-based method) was developed to enhance motion robustness [12]. [PERSON] et al. (2017) introduced the plane-to-orthogonal-skin (POS) approach, which isolates pulse signals by establishing a projectionplane perpendicular to the skin tone within the RGB color space [13]. [PERSON] et al. (2011) separated the RGB color signal to estimate cardiac activity using independent component analysis (ICA), and [PERSON] et al. (2011) employed principal component analysis (PCA) and demonstrated that it reduced computational complexity while preserving accuracy compared with the ICA approach [14; 15]. Deep learning has also been incorporated to extract pulse signals effectively. [PERSON] et al. (2016) proposed 2 SR (i.e., spatial subspace rotation), a data-driven algorithm designed to estimate the spatial subspace of skin pixels and analyze their temporal changes to determine the HR [16]. [PERSON] et al. (2018) introduced DeepPhys, which uses end-to-end convolutional networks to learn facial color changes as time-series data and to estimate pulse signals [17]. In rPPG recordings, movements of the head, face, or other body parts can cause unwanted variation or distortion in the rPPG signal. This motion artifact can reduce the accuracy of heart rate measurement. Most recent research has focused on effectively rejecting this motion noise and accurately extracting pulse signals. [PERSON] (2024) showed that rPPG spectral filtering techniques that divide and analyze signals in the frequency domain and wavelet transforms can reduce noise and reconstruct pulsed signals [18]. [PERSON] et al. (2017) developed amplitude-selective filtering (ASF) to detect signs of cardiac activity [19]. This method uses an R-channel, which is susceptible to high-amplitude motion artifacts, potentially impacting the signal. The amplitudes that fall outside a specified threshold range are regarded as noise. They also suggested that the color distortion function distinguishes between color changes induced by pulses and noise [20]. The relative pulsatile amplitude, which represents the characteristics of the rPPG signal according to the red (R), green (G), and blue (B) channels, has been employed in previous studies. However, spatial redundancy, which represents the characteristics of the rPPG signal appearing in the entire face area, has not been studied yet [21]. In this study, a machine learning approach was developed to refine the rPPG signal by effectively isolating signal components with prominent cardiac activity.