# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 4 Experimental Results
### Results of Respiration Estimation in a Restricted Movement Environment  
To verify the respiration measurement performance of our proposed method, we conducted an experiment to compare the results of estimating the respiratory signal and rate with the reference using test data. To confirm the respiration measurement performance in ideal situations where movement is strictly controlled, we conducted experiments only on scenes #1 and #2 of the test data. Details of the experimental results can be found in Tables 4 and 5, and the Bland-Altman plot results are shown in Figure 11. According to the experimental results, the MAE of the method proposed in scene #1 was the most accurate at 0.084, and that of the optical flow-based method was the most accurate at 0.123 in scene #2. When the data from scenes #1 and #2 were used, the optical flow-based method showed the highest performance at 0.107. We confirmed through ICC that the proposed method and the optical flow-based method exhibited excellent bpm estimation performance. Although the optical flow-based method showed the best performance in the experiment when movement was controlled, the difference was potentially not significant because bpm was calculated with a precision of a 0.36 level due to the nature of the frequency analysis  
Figure 10: Comparison of respiration estimation accuracy and computational time according to window size change. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Window** & **MAE (bpm)** & **Inference** & **Window** & **MAE (bpm)** & **Inference** \\
**Size (s)** & **MAE (bpm)** & **Time (ms)** & **Size (s)** & **MAE (bpm)** & **Time (ms)** \\ \hline
1 & 5.5430 & 0.3314 & 16 & 0.1859 & 0.8533 \\
2 & 3.2044 & 0.4635 & 17 & 0.1848 & 0.8882 \\
3 & 1.8921 & 0.6130 & 18 & 0.2144 & 0.8594 \\
4 & 1.2553 & 0.7679 & 19 & 0.2383 & 0.8625 \\
5 & 0.9901 & 0.7876 & 20 & 0.2193 & 0.8385 \\
6 & 0.8733 & 0.7889 & 21 & 0.2068 & 0.8442 \\
7 & 0.6581 & 0.7971 & 22 & 0.2345 & 0.8951 \\
8 & 0.5102 & 0.7986 & 23 & 0.2534 & 0.8564 \\
9 & 0.4739 & 0.7920 & 24 & 0.2382 & 0.9244 \\
10 & 0.4603 & 0.8154 & 25 & 0.2129 & 0.9412 \\
11 & 0.3785 & 0.8468 & 26 & 0.2277 & 0.8854 \\
12 & 0.3145 & 0.8501 & 27 & 0.2148 & 0.8739 \\
13 & 0.2869 & 0.8260 & 28 & 0.2263 & 0.9089 \\
14 & 0.2441 & 0.8311 & 29 & 0.2579 & 0.8530 \\
15 & 0.2350 & 0.8423 & 30 & 0.2524 & 0.9211 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The changes in the accuracy and inference time according to window size. method. Both methods showed the small error of a 0.1 bpm level and an exactly accurate respiration measurement was possible. Furthermore, a slight degradation in performance was observed at a greater distance than at a closer distance, which can be attributed to the greater influence of optical noise with increasing distance. However, even at a distance of 1.5 m, the respiration measurement methods exhibited good accuracy. The proposed method and optical flow-based method recorded small MODs and LOAs in all situations, which means that the error bias was small and the agreement between the reference and the data estimated through the two methods was remarkably high. The Bland-Altman plot in Figure 11 shows that the two methods had little error bias, and the agreement between the data was also high. Each point in the plot is distributed discretely by bpm calculation precision, and the density of the overlapped points is shown in color (red indicates higher density and blue lower density). Table 5 shows the results of the statistical verification of the difference between the bpm error group of the proposed method and the bpm error group of the existing methods through a \(t\)-test, and there was no statistically significant difference between the optical flow-based method and the proposed method for all motion-controlled situations. Intensity-based methods recorded low performance values in all the experimental results. This can be attributed to the limitations of the pixel variation-based method mentioned in Section 2.2.