# Camera-Based Vital Sign Estimation Techniques and Mobile App Development
## 1 Introduction  
Recently, research has been conducted using camera images to extract the change components caused by the expansion and contraction of blood vessels as noncontact remote PPG (rPPG) signals. In a reflection model of an rPPG signal [1], some of the ambient light incident on exposed skin, such as that of the face, is specularly reflected from the skin's surface, and the remaining light is absorbed by the skin (see Supplementary Figure S1). Some of the absorbed light is reflected by blood vessels and exits the skin through diffuse reflection. The regular component of reflection from the skin's surface and diffuse reflection component, including the pulsation of the blood flow in the blood vessels, are acquired as camera images. The rPPG signal is extracted from the obtained image, and the heart rate (HR) is extracted from the diffuse reflection component caused by the pulsation of the blood vessels. The methods for obtaining the HR using an RGB camera can be classified into blind source separation (BSS)- and model-based methods. BSS-based methods can be further classified into independent component analysis (ICA) [2, 3] and empirical mode decomposition [4]. [PERSON] et al. [2] and [PERSON] et al. [3] detected an area of a face as a region of interest in RGB images and extracted the average signal of each RGB channel in the frame units. The obtained RGB signal was converted into three separate, uncorrelated sourcesthrough ICA, and a second signal with a separate source was used as an HR waveform. [PERSON] et al. [4] calculated the HR by counting the peaks of the fourth intrinsic mode function (IMF) obtained using ensemble empirical mode decomposition (EEMD) from the G-channel reflection in a forehead ROI. The ICA method identifies the HR by separating the RGB signals from an image of a face into independent, uncorrelated signals, whereas the model-based method [5] uses a color model for extracting rPPG signals from color signals. In particular, the model-based method uses the minute skin color changes in the face due to the blood flow generated by a heartbeat. Most model-based methods use color models, which employ G [5], green-red difference (G-R or GRD) [5], adaptive GRD (AGRD) [6], LAB [7], skin color stratification [8], chrominance (CHROM) [9], and plane-orthogonal-to-the-skin (POS) methods [10]. The G method estimates the HR using only the G-channel signal, which is affected the most by the hemoglobin absorption in the blood [5]. In the G-R or GRD methods, the rPPG signal is obtained by subtracting the R-channel signal, which changes the least, from the G-channel signal, which changes the most significantly. In addition, the AGRD method uses the adaptive color difference between the green and red channels and an adaptive bandpass filter (BPF) to remove residual motion artifacts from the rPPG signal [6]. In the LAB method [7], EEMD and an IMF are applied to the difference between the A and B channels in the LAB color space, while the skin color amplification method [8] detects the HR by amplifying the skin color in an RGB image of the ROI. The CHROM method [9] extracts rPPG signals using a combination of color difference signals from the RGB channels. The POS method [10] creates new projection axis coordinates by combining three orthogonal R, G, and B coordinates and projects temporally normalized R, G, and B values onto the new projection axis to create a new two-dimensional color difference signal. Recently, [PERSON] et al. [11] proposed a method for estimating heart rate from rPPG signals under irregular frame rates and data loss conditions using low-complexity timing correction techniques (LCTC), including linear, cubic, and filter interpolation. In addition, [PERSON] et al. [12] introduced a 3D Convolutional Neural Network (3D CNN) designed to consider both long-term periodic characteristics and short-term temporal features for effective rPPG signal extraction.