# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 2 Related Works  
### Photoplethysmography-Based Respiration Measurement Method  
Both respiration and pulse signals can be observed or extracted from photoplethysmography signals. The decrease in intrathoracic pressure that occurs during inspiration affects venous blood pressure and ventricular volume, which promotes venous blood return and changes cardiac output [31]. In addition, increased intrathoracic pressure due to respiration stimulates the vagus nerve and the sinus node that controls the heart rate, causing respiratory sinus arrhythmia, a phenomenon that lowers the heart rate [32]. Photoplethysmography (PPG) is based on the optical property that the circulation changes the amount of hemoglobin molecules and proteins in the blood, thereby changing the degree of light absorption and scattering across the light spectrum. Therefore, PPG technology can be used to measure the degree of changes in capillary blood due to heartbeat through an optical sensor attached to the surface of the skin [33]. Even without using a sensor attached to the skin surface, these pulse changes can be measured in a noncontact approach using an optical sensor, such as an RGB webcam [34; 35]. The acquired remote PPG signal includes amplitude and period changes due to respiration, and respiration can be measured using a noncontact method by analyzing changes in heart rate components due to respiration. Cases and previous studies verified that information on respiration can be estimated using this method [36; 37]. In 2010, [PERSON] et al. separated PPG signal and noise components using independent component analysis (ICA) of the average change in brightness of the signal of each RGB channel in the face region and extracted respiration from the power spectrum of the heart rate variability, which is the change in the interval between heartbeats [38; 39; 40]. However, although the method of estimating respiration rate using heart rate variability may be effective for young and healthy subjects, it is difficult to apply to older patients or those with chronic disease [41]. In 2011, [PERSON] et al. demonstrated measuring respiration rate in a sports situation by applying a change in the reflected light component of the forehead and blind source separation (BSS) for continuous monitoring of respiration [42]. In addition, [PERSON] et al. obtained the average of each component of the face and each RGB component of the neck region and evaluated the performance of a BSS-based respiration measurement method of the signal of the changes in the average of eight subjects [43]. [PERSON] et al. detected a face region from video and classified the face, upper body, and background using a nonparametric Bayesian image segmentation algorithm and then used an autoregressive model to recognize RGB changes in the face region to estimate respiration information from the corresponding signal [44]. In 2019, [PERSON] applied independent component analysis (ICA) to each RGB component of face images to separate the noise and PPG components and decomposed the signal into intrinsic mode functions through complete ensemble empirical mode decomposition with adaptive noise, as well as considerably improved the performance of respiration measurement by selecting the signal that best represented the true respiration [45]. In addition, [PERSON] et al. reviewed a method to convert a detected face image into an HSV color model to measure respiration with robustness of changes in light and measured respiration using changes in the color component of a color model. They conducted an experiment on 25 adult subjects (15 men and 10 women) and showed that their approach may be considered promising for application in telemedicine [46]. In 2016, [PERSON] et al. tracked feature points in an image of the face to improve measurement performance in visible light, as well as infrared environments, by observing changes in the components of light reflected by the skin and estimating signals through the weighted sum of the changes.