## 4 Discussion
As proved by previous studies, the best results of breathing signals analysis are achieved if nostrils are clearly visible in a frame [52] to be able to capture temperature modulation during breathing events. Also, various methods are often used to remove possible motion artifacts, e.g., video stabilization [53]. However, in our work we wanted to minimize computation overhead and limit the requirements about data acquisition setup to provide fast responses about the health status of the monitored person. Specifically, we assumed basic data acquisition setting (no need of tilting head backward to improve visibility of nostril area) and minimal data pre-processing step (no additional motion/lightning compensation algorithms). In this way, we wanted to make sure that the system could be used by non-professionals, without the supervision of the third person and calculate results on the resource-constrained devices. The analysis performed for various thermal sequence resolutions, showed that the gain of the RR estimation accuracy can be achieved by applying SR deep neural network on low-resolution thermal images. For all estimators and aggregation operators the lowest RMSE values were achieved for the proposed DL model with the scaling factor of 2. For the SR model, scale 2, the Lepton dataset, average aggregator, RMSE of RR accuracy was reduced by 0.77 (8-bit data) comparing to bicubic and by 0.08 (8-bit data) comparing to original sequences if \(eRR_{sp}\) was used. In case of the \(eRR_{ss}\) estimator, these differences are 4.19 and 10.66, respectively. For the SR model, scale 2, the SC3000 dataset, average aggregator, RMSE of RR accuracy was reduced by 3.41 (8-bit data) comparing to bicubic and by 0.54 (8-bit data) comparing to original sequences if \(eRR_{sp}\) was used. In case of the \(eRR_{ss}\) estimator, these differences are 11.49 and 11.63, respectively. For the scale 4, similar conclusion is true for the \(eRR_{sp}\) estimator. For the SR model, scale 4, the Lepton dataset, average aggregator, RMSE of RR accuracy was reduced by 0.65 (8-bit data) comparing to bicubic and by 0.01 (8-bit data) comparing to original sequences if \(eRR_{sp}\) was used. In case of the \(eRR_{as}\) estimator, these differences are 1.16 and 8.20, respectively. For the SR model, scale 4, the SC3000 dataset, average aggregator, RMSE of RR accuracy was reduced by 2.25 (8-bit data) comparing to bicubic and it was as good as original sequences if \(eRR_{sp}\) was used. In case of the \(eRR_{as}\) estimator, these differences are 3.69 and 3.83, respectively. During the experiments, we found out that the \(eRR_{as}\) estimator is very sensitive to the selected ROI, resulting in unreliable RR values that led to a significant increase of RMSE. Thus, results for this estimator are inconclusive and should be further investigated in future studies. In addition, for bicubic and EVM, 16-bit data turned out to be beneficial in improving accuracy of RR estimation. This indicates the need for providing lossless conversion of raw radiation values to temperature data. In case of SR, although higher PSNR and SSIM values were achieved for 16-bit sequences, this improvement hasn't led to better accuracy of RR estimation. This result may be caused by the difficulty of DL network training. Both 8-bit and 16-bit models were trained using the same hyperparameters, while the 16-bit network had to deal with more information. As a result, it may have overfitted to the training set or suffered from the vanishing gradient problem, what may have caused worse RR estimation accuracy. This issue will be further investigated by us in the future work. The important finding of our study is that the proposed SR model outperformed the EVM algorithm on 8-bit and 16-bit data with scale 2 for all estimators and aggregation operators. For scale 4 the SR network was also better than EVM in most of the cases.