## 4 Results
### Feature Analysis  
In order to analyze the features used by the trained model for prediction based on the observed differences in the output values between the individual and ensemble models in Section 4.1, SHAP (Shapley additive explanations) was employed [26]. SHAP is based on the concept of Shapley values, which is derived from cooperative game theory. Shapley values measure the worth of each player in a cooperative game. When applied to evaluating feature importance in machine learning models, SHAP values provide insights into the impact of features on model predictions. The range of SHAP values varies depending on the characteristics of the features and the model's structure. The absolute magnitude of a SHAP value represents the contribution of each feature, indicating the extent to which it influences the model's prediction. A larger SHAP value indicates a greater impact of the corresponding feature on the model's prediction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & **Correlation Coefficient** & \multirow{2}{*}{**MAE**} & **RMSE** & **ICC** &
\begin{tabular}{c} **95\% Confidence** \\ **Interval** \\ \end{tabular} \\ \hline
1 & 0.579 & 1.558 & 1.922 & 0.664 & 0.577–0.733 \\
2 & 0.669 & 2.125 & 3.199 & 0.750 & 0.682–0.803 \\
3 & 0.516 & 2.980 & 3.638 & 0.636 & 0.467–0.742 \\
4 & 0.817 & 3.014 & 3.465 & 0.725 & \(-\)0.189–0.907 \\
5 & 0.512 & 1.935 & 2.555 & 0.586 & 0.293–0.737 \\
6 & 0.767 & 1.044 & 1.257 & 0.678 & \(-\)0.195–0.882 \\
7 & 0.572 & 1.490 & 2.040 & 0.620 & 0.519–0.700 \\
8 & 0.688 & 2.214 & 2.934 & 0.603 & 0.389–0.729 \\
9 & 0.269 & 1.165 & 1.570 & 0.369 & 0.174–0.513 \\
10 & 0.723 & 0.630 & 0.858 & 0.816 & 0.756–0.859 \\
11 & 0.756 & 1.157 & 1.586 & 0.827 & 0.713–0.887 \\
12 & 0.577 & 1.224 & 1.560 & 0.687 & 0.567–0.768 \\
13 & 0.671 & 1.308 & 1.706 & 0.787 & 0.709–0.841 \\
14 & 0.698 & 2.241 & 2.772 & 0.783 & 0.612–0.865 \\ Mean & 0.630 & 1.720 & 2.219 & 0.681 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical results 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 Coefficient** & \multirow{2}{*}{**MAE**} & **RMSE** & **ICC** & \begin{tabular}{c} **95\% Confidence** \\ **Interval** \\ \end{tabular}  \\ \hline
1 & 0.727 & 1.812 & 2.313 & 0.613 & 0.112–0.797 \\
2 & 0.231 & 4.715 & 6.120 & 0.363 & 0.191–0.498 \\
3 & 0.626 & 2.853 & 3.200 & 0.590 & 0.085–0.783 \\
4 & 0.845 & 2.343 & 2.939 & 0.854 & 0.697–0.917 \\
5 & 0.821 & 2.837 & 3.336 & 0.706 & 0.376–0.836 \\
6 & 0.440 & 1.775 & 2.210 & 0.428 & 0.105–0.617 When calculating the SHAP values for each feature, all combinations of features are considered to evaluate the model's predictions. In other words, the difference between the model's predictions when a feature is excluded and the original predictions is computed, and this difference represents the importance (SHAP value) of that feature. Figures 13-15 depict the SHAP values and feature contributions for the individual models trained and tested on the data used in Figures 10-12. These visualizations were used to confirm whether the features with dominant contributions significantly impacted each individual participant. Figures 13-15 indicate significant variations in the importance of the features among different subjects. Furthermore, since these graphs demonstrate the contribution to the data used for training, it is confirmed whether the feature with the predominant contribution was important to each subject by confirming the contribution to the data used in testing. Figures 16-18 show the results, displaying the SHAP values for models trained on input data for 10 s, 20 s, and 30 s.  
Figure 14: SHAP values of the personalized model used in Figure 11.  
Figure 13: SHAP values of the personalized model used in Figure 10. The \(x\)-axis represents the features and the \(y\)-axis shows the contribution according to the features.