# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 5 Discussion and Limitations  
Recent studies on noncontact respiration measurement were widely adopted following the interest of noncontact heart rate measurement. Nevertheless, to the best of our knowledge, the proposed approach is the first to measure respiration in a fully automated manner, and previous studies needed a pre-defined torso RoI for respiration measurements and only developed an advanced respiratory signal extraction algorithm. The proposed method detects up to the torso area and extracts respiratory signals from the detected RoI. In addition, our proposed respiration measurements have a processing speed of less than 30 ms, even in a CPU environment, which generalizes noncontact respiration measurement and presents new directions for applications in various fields. In addition to complete automation of remote respiratory rate measurements, the results of several experiments verified that noise removal, which was is not solved by existing methods, can be successfully performed through deep learning methods that infer the rate of respiration rate in motion noise environments. Therefore, the proposed method is designed to be reliable and robust to motion noise. The proposed method can be widely adopted and can also be executed on inexpensive and commonly used webcams and laptops, in contrast to existing methods. However, our proposed method is an early version of the real-time automatic respiration measurement technology with model-based motion analysis technology, and continuous improvement remains needed to develop the techniques that make up the proposed method further. First limitation: Our approach can be expanded to simultaneously measure multiple breaths in a single image. Moreover, there is a need for the optimization and verification of the various structures and scales that the proposed CNN model can process. Second limitation: Further experiments can be conducted by acquiring more data to improve the performance of the model and additional augmentation can be applied. In addition, it is necessary to verify the performance of various factors, such as measurement distance, measurement posture, color, and type of clothing worn by the subject, and additional experiments can be conducted on changes in the movement of objects covering the RoI. In addition, because the frequency analysis-based method used to estimate the respiratory rate uses the strongest frequency component as the respiratory rate at a window of 17 s, reflecting changes in respiration may be difficult if a subject's respiratory pattern changes rapidly. Because the proposed method can estimate stable respiratory signals as well as measure the respiratory rate, it can be extended to detect additional information (depth of respiration, apnea interval, etc.) that can be estimated from respiratory signals. Third limitation: The respiration signal extraction is affected according to the ROI selection method. The proposed method uses the body detector to measure respiration in torso ROI, so the measurement results may \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **RoI** & **Motion Mean-** & **Signal** & **RR** & **Total** \\  & **Detection** & **surement** & **Refinement** & **Estimation** & **Total** \\ \hline Time(ms) & 24.45 & 4.58 & 0.17 & 0.29 & 29.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Inference time of each respiration estimation on CPU. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **RoI** & **Motion Mean-** & **Signal** & **RR** & **Total** \\  & **Detection** & **surement** & **Refinement** & **Estimation** & **Total** \\ \hline Time(ms) & 19.57 & 1.96 & 0.13 & 0.20 & 21.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Inference time of each respiration estimation on GPU. vary depending on the performance of the body detector.