## 4 Discussion  
The proposed system succeeded in measuring RR for subjects at rest in different positions. In Table 5 the quality metrics used in the reviewed works and in our proposal are shown. It is clear that using the MCV strategy, estimation was in close agreement (\(\approx\)98%, bias \(=0.347\pm 1.45\) ppm) with the reference obtained by visual counting in contrast to the MV, where the agreement fell to \(\approx\)97% (bias \(=0.33\pm 2.1\) ppm) and to the OV strategy, where the agreement fell to \(\approx\)96% (bias \(=-0.01\pm 3.468\) ppm). Hence, it is observed that the difference error with respect to the reference based on the MCV strategy fell to \(<\pm 2\) ppm with a MAE of \(3.28\pm 3.33\)% while the MV strategy fell to \(<\pm 3\) ppm with a MAE of \(3.28\pm 3.33\)%. Figure 6: Bland–Altman plot obtained considering all the subjects: black line is the MOD, red lines are the LOAs. (**a**) Using the original video. (**b**) Using the magnified video. (**c**) Using the reconstructed motion components. Figure 7: Bland–Altman plot obtained for each position: black line is the bias, red lines are the Limits of agreements. (**a**) ‘LD’ position. (**b**) ‘LU’ position. (**c**) ‘S’ position. (**d**) ‘F’ position. \(3.15\pm 5.05\%\) and the OV strategy fell to \(<\pm 4\) bpm a MAE of \(6.84\pm 9.79\%\). We observe then that the use of the magnification process and particularly the use of magnifying components instead of the original video or the magnified video improves detection. The use of the magnification process can produce artifacts in the video, but if we take only the magnified components, the presence of these artifacts is minimized. Concerning the position of the subject using the MCV strategy, the results were in close agreement for the 'S' position (\(\approx\)99%, \(\text{bias}=0.157\pm 0.742\) bpm) and 'LD' position (\(\approx\)99%, \(\text{bias}=0.108\pm 0.84\) bpm) and fall for 'F' position (\(\approx\)98%, \(\text{bias}=0.602\pm 1.80\) bpm) and 'LU' position (\(\approx\)97 %, \(\text{bias}=0.620\pm 2.21\) bpm). These results confirm that our strategy can be used for different positions despite some variability in the agreement. Compared to other recent works using Bland-Altman analysis, the work of [PERSON] et al. [20] obtains an agreement of \(\approx\)98% (\(\text{bias}=-0.01\pm 1.01\)) falling to a difference error with respect of the reference \(<\pm 2\) bpm, consistent with our results. The work of [PERSON] et al. [30] obtains an agreement of \(\approx\)99% (\(\text{bias}=0.21\pm 0.62\)) falling to a difference error with respect to the reference \(<\pm 1\) bpm, consistent with our work. The two latter methods are dependent on the choice of the ROI in contrast to our CNN-Whole-Image strategy, which is independent of the choice of the ROI. In addition, our approach uses the tagged inhalation and exhalation frames as reference for training the CNN as opposed to other strategies that use a reference obtained by means of a contact standard sensor. Some reviewed works did not use the Bland-Altman analysis as quality metric to compare to other strategies. As shown in Table 5, [PERSON] et al. [25] obtained a RMSE of 0.05 consistent value compared to our ROI strategy (RMSE = 0.41) and to our CNN-Whole-Image strategy (RMSE = 0.85). [PERSON] et al. [26] proposed a method that did not need to use a ROI to obtain a MAE of 20.11% greater than our CNN-Whole-Image strategy with a MAE of \(3.28\pm 3.33\%\). [PERSON] et al. [24] proposed a method based on an automatic choosing of the ROI obtain a MAE of 15.4% greater than our two strategies. Finally, [PERSON] et al. [18] obtained a MAE of 3.02 bpm greater than our ROI strategy (MAE = 0.32 bpm) and to our CNN-Whole-Image strategy (MAE = 0.56 bpm). Some of the limitations of this work are the limited number of subjects for the statistical analysis. The influence of the distance of the camera for all the tests was not studied.