# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 6 Conclusions  
In this study, we proposed a contactless breathing measurement method that can universally utilize standard RGB cameras. Existing contactless breathing measurement methods are not widely adopted because of the need for a separate device, difficulty in automatically detecting RoI, and limitations in that measurement is impossible when unintended movement occurs. In this study, it is shown that the entire process can be automated using only RGB cameras for respiration measurements and the technology to automatically detect RoI, and the existing limitations are improved by using a CNN-based method designed to detect respiratory movements with high resistance to noise. Segmentation technology and lightweight body landmark-based technology were presented as alternatives to the existing RoI detection technology, and an environment in which the two methods could be applied was considered.  
For respiratory motion analysis, we proposed a model that learns the movement occurring in two RoIs by applying various augmentation techniques to improve its performance. We also presented a method for purifying noise components that may be included in a motion signal, and we applied a method to detect respiratory rates from the purified signal. The performance of the proposed method was compared with representative existing methods based on optical flow and pixel intensity. The proposed method enables accurate respiration measurement with an error of approximately 0.1 ppm and did not show performance degradation even in a noisy environment that notably degraded the performance up to 8 ppm errors in existing methods. It can also operate in real time about 30 FPS and need 0.7 GFLOPs for inference. This shows that it can be applied not only to a CPU environment but also to a mobile device. Our experimental results show that the proposed method can be implemented in various applications to overcome existing limitations. However, the problem of performance degradation due to distance still needs further investigation, and further analysis remains necessary because we analyzed the effect of the movement of the measurement target on performance, rather than that of movements occluding the RoI.  
Conceptualization, E.C.L.; methodology, E.C.L. and H.H.; validation, K.L.; investigation, H.H.; data curation, H.H.; writing--original draft preparation, K.L.; writing--review and editing, E.C.L. and K.L.; visualization, H.H.; supervision, E.C.L.; project administration, E.C.L.; funding acquisition, E.C.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a 2020 Research Grant from Sangmyung University.  
Based on the 13-1-3 of the Enforcement Regulations of the Act on Bioethics and Safety of the Republic of Korea, ethical review and approval were waived (IRB-SMU-S-2021-1-005) for this study by Sangmyung University Institutional Review Board, because this study uses only simple contact measuring equipment or observation equipment that does not follow physical changes.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The obtained data cannot be shared because it was agreed that it could be used only for this study.  
The authors declare no conflict of interest.