## 4 Results  
To quantitatively evaluate the performance of the trained model in predicting oxygen saturation, we used the Pearson correlation coefficient, mean absolute error, and root mean square error. The Pearson correlation coefficient is one of the methods used to measure the linear correlation between two variables, indicating a strong correlation between the variables when the absolute value is close to 1. The mean absolute error represents the average error between the predicted and actual values, and smaller values indicate better predictive performance of the model. The root mean square error represents the square  
Figure 6: CNN model structure based on ResNet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**Correlation**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**RMSE**} \\  & **Coefficient** & & \\ \hline
1 & 0.586 & 0.825 & 6.338 \\
2 & 0.512 & 3.062 & 3.816 \\
3 & 0.696 & 1.760 & 2.373 \\
4 & 0.  
The results indicate that the trained model did not perform well in predicting oxygen saturation compared to previous studies. However, it is important to note that previous studies on the non-contact estimation of oxygen saturation evaluated the results by averaging the individual training and testing results for each participant. Taking this into consideration, our study also conducted training and testing on a participant-specific basis. Figures 10-12 show selected graphs comparing the oxygen saturation estimated using the model trained with data from 10, 20, and 30 s, respectively, with the oxygen saturation measured using the contact-based sensor. The complete results for all participants are summarized in Tables 6-8. The intraclass correlation coefficient (ICC), which measures the agreement between two evaluations, ranged between 0 and 1.  
Figure 11: Predicted oxygen saturation and actual oxygen saturation of a model trained using 20 s of signals as the input data (personalized model).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & **Correlation** & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**ICC**} & **95\% Confidence** \\  & **Coefficient** & & & & **Interval** \\ \hline
1 & 0.498 & 1.487 & 1.874 & 0.592 & 0.487–0.675 \\
2 & 0.513 & 1.774 & 3.186 & 0.562 & 0.369–0.686 \\
3 & 0.693 & 2.212 & 2.684 & 0.763 & 0.524–0.862 \\
4 & 0.759 & 3.990 & 4.510 & 0.601 & \(-\)0.213–0.852 \\
5 & 0.451 & 1.343 & 1.922 & 0.615 & 0.517–0.693 \\
6 & 0.653 & 1.741 & 1.973 & 0.434 & \(-\)0.196–0.751 \\
7 & 0.509 & 2.378 & 2.828 & 0.375 & \(-\)0.184–0.662 \\
8 & 0.620 & 1.718 & 2.279 & 0.677 & 0.597–0.742 \\
9 & 0.411 & 0.872 & 1.374 & 0.297 & 0.005–0.494 \\
10 & 0.576 & 0.357 & 0.694 & 0.714 & 0.639–0.773 \\
11 & 0.713 & 1.082 & 1.444 & 0.770 & 0.710–0.817 \\
12 & 0.403 & 2.735 & 3.238 & 0.353 & 0.004–0.564 \\
13 & 0.496 & 1.267 & 1.709 & 0.538 & 0.418–0.632 \\
14 & 0.684 & 1.618 & 2.255 & 0.743 & 0.631–0.815 \\ Mean & 0.570 & 1.755 & 2.284 & 0.574 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical results of a model trained using 10 s of signals as the input data (personalized model).  
Figure 12: Predicted oxygen saturation and actual oxygen saturation of a model trained using 30 s of signals as the input data (personalized model).  
Upon examining the results, it can be observed that the individual models performed either better or comparably to previous studies, and there was a significant difference in exactness compared to the ensemble model. In Section 4.2, the reasons for the discrepancies in the output values between the individual and ensemble models are analyzed.