## 2 Related Works  
Recently, methods have been proposed to measure oxygen saturation non-invasively using facial or hand images. These methods utilize specialized cameras with filters capable of capturing specific wavelength bands [11] or commonly available RGB cameras such as webcams or smartphone cameras. While specialized cameras can capture wavelength bands suitable for measuring oxygen saturation, they are not widely used. Therefore, there is a need for methods that can measure oxygen saturation using RGB cameras. Previous studies that utilize RGB cameras for measuring oxygen saturation have primarily employed the ratio of ratios (RoRs) method. [PERSON] et al. [12] and [PERSON] et al. [13] estimated the AC and DC components for red and blue wavelengths from the facial region of the RGB image and calculated the RoRs to measure oxygen saturation. [PERSON] et al. [14] estimated the AC and DC components for red and green wavelengths from the dorsum of the hand region in the RGB image and calculated the RoRs to measure oxygen saturation. Additionally, some methods go beyond using two wavelength bands and utilize all three channels of the RGB image. [PERSON] et al. [15] estimated the AC and DC components by using all three wavelength bands from the facial region of the RGB image, while [PERSON] et al. [16] estimated the AC and DC components by using all three wavelength bands from the palm region of the RGB image and calculated the RoRs to measure oxygen saturation. Furthermore, methods that use convolutional neural networks (CNNs) to measure oxygen saturation have been proposed, which differ from previous regression-based methods. [PERSON] et al. [17] calculated the AC and DC components using the red, green, and blue wavelengths from the facial region of the RGB image and fed the resulting spatiotemporal map into a CNN model to measure oxygen saturation. Various papers have proposed methods using RGB cameras to measure oxygen saturation, with most considering the AC and DC components. However, the methods for estimating the AC and DC components from the photoplethysmography (PPG) signal, despite the clear relationship between RoRs and oxygen saturation, vary among the papers. In the examined papers, [PERSON] et al. [12] calculated the AC component as the average of the peaks and valleys in the PPG signal and the DC component as the mean of the PPG signal. [PERSON] et al. [13] calculated the AC component as the standard deviation of the PPG signal and the DC component as the mean of the PPG signal. [PERSON] et al. [14] applied band-pass filtering with a bandwidth of 0.5-5 Hz to obtain the AC component and low-pass filtering with a cut-off frequency of 0.3 Hz to obtain the DC component. [PERSON] et al. [15] performed band-pass filtering with a bandwidth of 0.6-3 Hz to obtain the AC component and low-pass filtering with a cut-off frequency of 3 Hz to obtain the DC component. [PERSON] et al. [16] used an eighth-order Butterworth filter with a bandwidth of 0.1 Hz centered around the estimated heart rate to obtain the AC component as the average of the peaks and valleys in the filtered signal and a second-order low-pass Butterworth filter with a cut-off frequency of 0.1 Hz to obtain the DC component as the median of the filtered signal. [PERSON] et al. [17] applied band-pass filtering with a bandwidth of 0.75-2.5 Hz to obtain the AC component and low-pass filtering with a cut-off frequency of 0.3 Hz to obtain the DC component. While extracting the AC and DC components is essential in the principle of estimating oxygen saturation, the performance of predicting oxygen saturation varies greatly depending on how these components are extracted [16]. Therefore, in this paper, we proposed a method for estimating oxygen saturation using RGB cameras without arbitrarily extracting the AC and DC components, utilizing the time-series data obtained from RGB images. Table 1 shows a summary of the related works.