# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 5 Discussion (and Future Work)  
The results indicate that our proposed rPPG screening method outperformed the existing approaches across all scenarios. Among them, random forest exhibited the best performance in both still and motion environments, while SVR achieved the highest accuracy in the overall dataset, where all the data were trained and tested together. For a more comprehensive analysis, we evaluated the accuracy for each individual. Table 4 presents the accuracy results for each person in the decision tree and random forest models, while Table 5 displays the accuracy for each person in the SVR and transformer models. Notably, the results demonstrate a substantial dependence on the individual's identity. Generally, individuals with lighter skin tended to exhibit higher infrared light saturation. Furthermore, individuals wearing glasses displayed lower accuracy, particularly in motion scenarios. Specifically, Person IDs 3, 8, 15, and 17 serve as examples of individuals wearing glasses. Finally, we compared the error size of the actual and predicted BPMs with the error size predicted by the model. Results of comparison are shown in Figure 11. Figure 11a illustrates a comparison example in a scenario considering the motion and talk of subject 2. In the bar graph, the red bar corresponds to the area predicted by the model to have low error and selected by the filter, while the black bar represents the area not selected by the model. The dotted plot displays the size of the predicted error at that time, which indicates that the red bars have the smallest values among all the bins. We compared the cPPG and predicted rPPG during the approximately 2 s interval (16-17 s) of the red bars that were selected by the filter, and the interval (64-65 s) that were not selected. The results are shown in Figure 11b,c, respectively. Regarding the first graph in Figure 11b which displays the PPG amplitude, the comparison of cPPG and rPPG through each interval indicates that rPPG, highlighted by a red line, has a similar spacing to cPPG. Below, there is a graph that displays the time-frequency spectrum of the 1-s signal's rPPG for both 16 and 17, with the correct value of cPPG represented by a red line [30]. It is evident that the time-frequency spectrum aligns precisely with the correct cPPG for both seconds 16 and 17. Figure 11c displays the time-frequency spectrum of the rPPG at 64 and 65 s, respectively, with the frequency of the cPPG shown as the red line. The unselected intervals exhibit a noisy  
Figure 10: Mean absolute error of each person ID by different scenarios (bpm). predicted signal, as the rPPG at 64 and 65 s does not have the same frequency spectrum as the maximum frequency of the cPPG. To summarize the contributions of this study, we have proposed a model to evaluate the confidence of rPPG measurement and proposed a screening method for rPPG through the confidence based on model's predicted error. The model for measuring confidence is a universal method that can be applied to any rPPG method because it uses facial region and landmark data and rPPG signal as inputs. Through experiments, it was confirmed and verified that performance is improved in all cases when the proposed method is applied to the Green [10] and DistancePPG [11] methods applicable to infrared environments. For the widespread use of rPPG, high-accuracy measurement is required even in wild environments such as vehicles, mobile, and fitness environments, and the proposed screening method is expected to be applied. However, this study has limitations. First, the dataset used in the experiment includes only Asian participants and was measured in an indoor environment. Therefore, additional experiments including participants of multiple races should be conducted as part of future works. The second is that this study utilized an experimental environment with no restrictions on subjects' movements and conversations, but its data were acquired and