# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 2 Related Works
### Motion-Based Respiration Measurement Method
obtained a motion matrix by applying optical flow to a video taken in 2015 and considered the respiration characteristics needed to detect RoIs related to respiration in units of pixels by assigning a score, and then estimated respiration information by fusing motion information [64]. They also showed that the distance from the camera or the generation of noise caused by the lighting environment can reduce measurement performance. In addition, [PERSON] et al. in 2019 confirmed that the optical flow-based method showed better performance by comparing the pixel change-based and optical flow-based methods [65]. In addition, after manually selecting the shoulder boundary region as the RoI, [PERSON] et al. [66] detected the movement of the region by calculating the differential in the longitudinal direction. A multi-task learning method for skin region segmentation was studied by [PERSON] et al., who refined various features extracted from a learning convolutional neural network (CNN) with BSS techniques, such as principal component analysis and ICA [67]. [PERSON] et al. also measured respiration through motion analysis, and other studies along these lines were conducted [68]. Although these motion analysis-based respiration measurement methods showed promise through experiments, they still involve two problems. The first is that no automated technology was applied to reliably detect RoIs to measure respiration. Failure to automatically detect a stable RoI not only implies a need to manually assign the RoI, but also requires extremely restricted movement so that the subject cannot deviate from the RoI. These methods are exceedingly difficult to apply to general-purpose measurement scenarios because the RoI must be periodically designated for continuous measurements when a subject moves. Although some studies estimated the area where movement due to respiration occurs as an RoI using the characteristics of respiration movement, this method is very vulnerable to motion noise, and its detection performance is thus unstable in environments with few motions, such as mobile and kiosk settings. In addition, [PERSON] et al. 's approach was also used to estimate a chest RoI based on the face area, but the exact area may not always be detected depending on the subject's posture [69,70]. An inaccurate RoI causes noise in the background to be included in the analysis target, and thus degrades the respiration measurement performance, which makes the respiration measurement technology difficult to apply without accurate RoI detection technology, which tends to be available only in limited environments. Second, while most studies demonstrated promising performance, most of these experiments did not consider real-world situations with considerable noise elements [71]. The movement of the hand covering the RoI is larger and stronger than the fine movements of breathing, and distinguishing between these movements and breathing movements by means of pixel variation or optical flow is challenging. Therefore, such noise may cause large deformations in the estimation of the respiratory signal process and may significantly reduce the accuracy of the respiration measurement. To enable respiration measurement technology to be widely adopted, the respiration measurement process must be automated, and the respiration measurement performance must be guaranteed even in a general environment. Therefore, in this study, we overcome these two limitations by automating RoI detection through the application of deep learning-based techniques and analyzing respiratory movements using CNN models trained to be robust to noise.