# Unobtrusive Respiratory Flow Monitoring Using a Thermopile Array: A Feasibility Study
## 4 Discussion
### Dataset B  
Constant breathing rates of 10 and 30 BPM both performed extremely well, resulting in an RMSE of 0 BPM for both the periodogram and AR model; a difference in the SNR can be noted between the two RRs since the respiration at 30 BPM resulted in a lower SNR compared to the other one. This was already expected considering the results obtained in Dataset A. Chirp had a median error of 1.41 BPM for the periodogram and 1.41 BPM for the AR model, whereas the pattern with sudden change resulted in the highest RMSE, 5.57 BPM and 6.57 BPM, respectively. This was expected since in the sudden change case when indecision between the two peaks was present (similar height of the peaks in FFT), an error of 20 BPM could be obtained. Moreover, the sudden change was the only case in which the AR performed particularly worse compared to the periodogram. This can be explained considering that the SNR in this case was significantly lower compared to the other four cases, and AR is known to perform well in cases of high or medium SNRs. In the spontaneous breathing case, the RMSE resulted in being around 0.98 for both SD estimators. Moreover, both correlation and Bland-Altman plots in Figure 7 show a bias in the estimated RR at 10 and 30 BPM. This bias was due to a delay in the detection of the transition between 10 and 30 BPM in the signals acquired with the sudden change pattern.  
The periodogram yielded an average RMSE of 1.56 BPM compared to 1.79 BPM of the AR model; the RE was 2.11% and 2.51%, respectively; and the SNR resulted in being on average 4.88 dB and 8.26 dB, respectively. The difference in error was on average only 0.23 BPM, which we considered insignificant; whereas, the SNR was always higher in the AR model case. The similarity in the results is justifiable considering that [PERSON]'s method binds AR and Fourier spectrum evaluated through FFT to have the same RR estimation, therefore limiting AR models to the same limitations of DFT. Autoregressive modeling can improve the results obtained in short windows, but a new method for order selection should be investigated.  
In this article, respiration detection using a thermopile array was proven to be feasible in a controlled environment and settings. It should be noted that this work is a proof-of-concept; future research should focus on proving the usability of such a sensor with a higher number of subjects and in clinical practice. Some limitations can be identified in the current approach, such as a lack of motion robustness and the pseudo-periodicity assumption for the respiration signal. Strategies to compensate motion are required. Considering that RGB cameras can currently monitor multiple vital signs and would be probably included in clinical practice in the future, a combination of RGB videos and thermopile array images could be used to track the identified pixel. A similar approach has been proposed in [11]; however, their solution relied on facial landmark detection; whereas, the pseudo-periodicity assumption would not hold in cases of apneas or other respiratory patterns. Strategies to overcome this challenge are definitely required before moving to clinical practice.