## 3 Results
### Respiratory Rate Estimation Using CNN-Whole-Image  
In Figure 5, we show the tagged results obtained for each approach with Trial 1 of Patient 7 in 'LF' position. The first row shows the tagged reference, where the x-axis indicates the frame number where the breathing changes from inhalation ('I') to exhalation ('E') or vice versa. The next rows show the tagged estimation for each approach, where both the MVC and MV approaches overcome the approach that uses the original video as input to the CNN. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **Ref.** & **CNN** & **Err. CNN (\%)** & **IPM** & **Err. IPM (\%)** \\ \hline
2 & 15 & 15.009 & 0.06 & 15.153 & 1.002 \\
3 & 21 & 20.678 & 1.533 & 22.320 & 6.285 \\
5 & 13 & 13.453 & 3.366 & 13.270 & 2.034 \\
7 & 13 & 12.926 & 0.569 & 12.957 & 0.386 \\
8 & 20 & 20.752 & 3.625 & 20.545 & 2.629 \\ \hline
**Mean** & 16.42 & 16.56 & 1.83 & 16.84 & 2.47 \\
**SD** & \(\pm 3.84\) & \(\pm 3.86\) & \(\pm 1.61\) & \(\pm 4.31\) & \(\pm 2.30\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of respiratory rate estimation (bpm) using the CNN-ROI proposal. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **RMSE (bpm)** & **MAE (bpm)** & **MAE (\%)** & **MOD (bpm)** & **LOAs (bpm)** & **MOD \(\pm\)** & **SCC, PCC** \\ \hline
\begin{tabular}{c} [PERSON] et al. [29] \\ \end{tabular} & - & - & \(2.47\pm 2.30\) & - & - & - & - \\ \hline [PERSON] et al. [20] & - & 0.39 & - & \(-0.01\) & \(+1,-1.02\) & \(-0.01\pm 1.01\) & - \\ \hline [PERSON] et al. [30] & - & - & 1.314 & 0.21 & \(+0.88,-0.46\) & \(0.21\pm 0.67\) & \(0.956,0.966\) \\ \hline [PERSON] [24] & - & - & 15.34 & - & - & - & - \\ \hline [PERSON] et al. [25] & 0.05 & - & - & - & - & - & - \\ \hline [PERSON] et al [26] & - & - & 20.11 & - & - & - & - \\ \hline [PERSON] et al. [18] & - & 3.02 & - & - & - & - & - \\ \hline Our proposal & 0.41 & 0.32 & \(1.83\pm 1.61\) & 0.16 & \(+1.01,-0.68\) & \(0.16\pm 0.85\) & \(0.91,0.99\) \\ using ROI & 0.85 & 0.56 & \(3.28\pm 3.33\) & 0.347 & \(+1.8,-1.1\) & \(0.347\pm 1.45\) & \(0.975,0.977\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metrics quality to evaluate the estimation of the RR. Later, we applied Equation (11) to the estimated temporal vectors obtained from the CNN. Results of this process are summarized in Table 6. The subject and the pose are listed in the first and second columns, respectively. Then, in columns three, four and five the estimations of RR from the reconstructed magnified components video (MCV) as input, the magnified video (MV) as input and the original video (OV) as input, respectively, are shown. Then, the reference RR for each subject is reported in column six. After that, it is the MAE for the three approaches in columns seven, eight, and nine. The MEA obtained by the CNN-Whole-Image proposal in the three approaches are \(3.28\pm 3.33\) for the MCV, \(3.15\pm 5.04\) for the MV and \(6.84\pm 9,79\) for the OV. The MAE obtained using the MCV strategy for different positions of the subject are: for the 'LD' position \(2.14\pm 1.17\%\), for the 'S' position \(1.51\pm 1.10\%\), for the 'LU' position \(6.14\pm 4.21\%\) and for 'LF' position \(4.63\pm 4.51\%\). The Bland-Altman method was used to assess the level of agreement between the experimental results obtained from the proposed system and those obtained from the reference. The 95% of limits of agreements were defined as MOD \(\pm 1.96\sigma\), where \(\sigma\) is the standard deviation of the differences. The bias is then MOD \(\pm 1.96\sigma\). In addition, the relationship between the estimated values and the reference was evaluated using Pearson's correlation coefficient (PCC), the Spearman correlation coefficient (SCC) and the root mean square error (RMSE).