# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 4 Results  
In order to evaluate the methods proposed in this study, the leave-one-out cross-validation method was employed [27]. The accuracy calculations were based on the data collected from all 18 participants, obtained through experiments conducted within the same controlled environment. In each iteration, the data from one participant were set aside as the validation set, while the data from the remaining 17 participants were utilized as the training set to fit the model. The model's predicted heart rate values were compared against the ground truth heart rate values, and the accuracy was measured using the _PTE6_ (percentage of time that the heart rate error is less than 6 bpm) calculation [28]. \[PTE6=\frac{\sum_{k}b_{k}}{K},\ \ \ b_{k}= \begin{cases}0,\ if\ \left|hr_{k}-\hat{hr}_{k} \right|>6\\ 1,\ if\ \left|hr_{k}-\hat{hr}_{k}\right|\leq 6\end{cases}  \tag{8}\]  
_PTE6_ is as shown in Equation (8), where \(hr_{k}\) represents the ground truth of heart rate corresponding to a \(k\)th time window. \(\hat{hr}_{k}\) is the remote heart rate measurement result measured in a \(k\)th time window. Additionally, mean absolute percentage error (_MAPE_) is used as a metric. _PTE_6 classifies success and failure based on 6 bpm, so _PTE_6 is not affected by outliers such as an error of 15 bpm or more. But, _MAPE_ has the characteristic of reflecting errors with large errors. _MAPE_ is expressed as Equation (9). \[MAPE=\frac{100}{k}\sum_{i=1}^{k}\left|\frac{y_{i}-\hat{f}(x_{i})}{y_{i}}\right| \tag{9}\]  
where \(k\) is the \(k\)th time window. \(y_{i}\) is the correct heart rate corresponding to the ground truth. \(\hat{f}(x_{i})\) is the remote heart rate measurement result. This process was repeated for all 18 participants, and the average results were computed to determine the overall accuracy. Among the four scenarios, the situations in which the subjects maintained a steady gaze and spoke while maintaining a steady gaze were grouped as still situations. The situation of moving their faces at various angles without speaking and moving their faces at various angles while speaking was grouped into motion situations. The results of calculating the accuracy using the two situations and all data are shown in the following Table 3.  
The results suggest that the rPPG screening models refine high-error segments of the video and selectively use low-error segments. We compared how accurately the model's predicted errors for each situation and person ID. Figures 9 and 10 demonstrate the predicted error values for each situation by person ID. As shown in Figure 9, the model's predicted error is usually high when there is motion and talk in most cases. This indicates that the model is adept at detecting instances in the input video that contain a considerable amount of noise, including motion, light, and SNR. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Still**} & \multicolumn{2}{c}{**Motion**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-8}  & & _PTE_6 & _MAPE_ & _PTE_6 & _MAPE_ & _PTE_6 & _MAPE_ \\ \hline \multirow{3}{*}{Without screening method} & Green [10] & 39.89\% & \(>\)15\% & 29.98\% & \(>\)15\% & 34.97\% & \(>\)15\% \\  & DistancePPG [11] & 54.08\% & 9.97\% & 45.44\% & 13.13\% & 50.43\% & 11.53\% \\  & Random Forest & 56.77\% & 9.46\% & 42.90\% & 12.4\% & 56.04\% & 10.92\% \\ \multirow{3}{*}{Green with screening method (Ours)} & SVR & 56.67\% & 9.14\% & 41.97\% & 11.83\% & 55.91\% & 10.75\% \\  & Transformer & 53.33\% & 10.27\% & 37.07\% & -15\% & 52.73\% & 11.44\% \\ \multirow{3}{*}{DistancePPG with screening method (Ours)} & Random Forest & 65.44\% & 7.76\% & 59.31\% & 8.54\% & 57.04\% & 8.94\% \\  & SVR & 61.26\% & 8.14\% & 56.09\% & 9.16\% & 62.27\% & 8.63\% \\ \multirow{3}{*}{DistancePPG with screening method (Ours)} & Transformer & 60.29\% & 8.23\% & 48.13\% & 11.73\% & 54.61\% & 9.45\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of original methods and proposed methods.