# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 4 Experimental Results
### Estimation of Respiratory Rate in a Free-Motion Environment  
To apply the proposed method in practice, stable performance in the event of motion noise is necessary, along with a thoroughly controlled environment. Therefore, we conducted an experiment on scenes #3 and #4, which were situations in which movements other than breathing occurred in the RoI. The experiment was conducted in the same manner as before, and the actual experimental results are shown in Tables 6 and 7 and Figure 13. The results show that the proposed method measured respiration accurately, and the values did not differ significantly from those obtained in the controlled environment. The optical flow-based method, which showed excellent respiration measurement performance in previous experiments, increased the overall MAE of the estimated bpm by a factor of more than 20, and the intensity-based method also decreased significantly. The correlation between the reference signal and the optical flow-based method also showed a low agreement of up to 0.4 in this experiment, in contrast to the high value of 0.9 or more obtained in the previous experiment. The ICC of the optical flow-based method was also quite low at 0.2 levels, unlike in previous experiments. The Bland-Altman plot in Figure 13 and the MOD and LOA results in Table 6 show that the error bias was large and the agreement between the estimated data was remarkably low. From the \(t\)-test results in Table 7, it may be confirmed that the proposed method shows a statistically significant error compared to all existing methods in a noisy environment. Figure 13: Bland-Altman plot of three respiration estimation methods for scene #3, #4: (**a**) ours, (**b**) optical flow-based, and (**c**) intensity-based. Table 8 shows the results of analyzing the statistical difference between the error group of bpm measured in the motion noise environment and the error group of bpm measured in the restricted motion environment for each noncontact respiration measurement method. Although the proposed method did not show statistically significant errors in the two situations, the results confirm that the other two methods showed statistically significant errors. Table 8 shows the results of analyzing the statistical difference between the error group of bpm measured in the motion noise environment and the error group of bpm measured in the restricted motion environment for each noncontact respiration measurement method. Notably, the proposed method did not exhibit statistically significant errors for the two situations, and the other two methods exhibited statistically significant errors. Figure 14 shows that reference and model-based breathing signals maintained stable waveforms at the time of motion noise, whereas optical flow and intensity-based methods showed significant damage to signal waveforms. Although the proposed method uses an image-based inference model that can respond to motion noise when it is included and learns from data containing information about motion noise, existing methods are vulnerable to motion noise. This experiment shows that the proposed method is much