## 3 Results
### Prediction of Physical Activity Through Machine Learning Models
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**ML Model**} & \multicolumn{2}{c}{**Glucose**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**F1-Score**} \\  & & **Monitoring Device** & & & & \\ \hline DT & FSLCstrip & 0.74 \(\pm\) 0.11 & 0.73 \(\pm\) 0.20 & 0.76 \(\pm\) 0.26 & 0.73 \(\pm\) 0.12 \\ DT & Eversense & 0.73 \(\pm\) 0.16 & 0.70 \(\pm\) 0.25 & 0.76 \(\pm\) 0.16 & 0.70 \(\pm\) 0.20 \\ DT & FSL & 0.65 \(\pm\) 0.17 & 0.58 \(\pm\) 0.17 & 0.73 \(\pm\) 0.31 & 0.63 \(\pm\) 0.16 \\ \hline KNN & FSLCstrip & 0.87 \(\pm\) 0.05 & 0.87 \(\pm\) 0.12 & 0.88 \(\pm\) 0.16 & 0.87 \(\pm\) 0.05 \\ KNN & Eversense & 0.90 \(\pm\) 0.08 & **0.93 \(\pm\) 0.13** & 0.88 \(\pm\) 0.16 & 0.91 \(\pm\) 0.08 \\ KNN & FSL & 0.85 \(\pm\) 0.07 & 0.87 \(\pm\) 0.06 & 0.82 \(\pm\) 0.16 & 0.85 \(\pm\) 0.06 \\ \hline LR & FSLCstrip & 0.83 \(\pm\) 0.06 & 0.81 \(\pm\) 0.11 & 0.85 \(\pm\) 0.22 & 0.83 \(\pm\) 0.04 \\ LR & Eversense & **0.91 \(\pm\) 0.06** & 0.90 \(\pm\) 0.07 & 0.91 \(\pm\) 0.17 & 0.91 \(\pm\) 0.05 \\ LR & FSL & 0.82 \(\pm\) 0.16 & 0.82 \(\pm\) 0.27 & 0.82 \(\pm\) 0.22 & 0.80 \(\pm\) 0.21 \\ \hline MLP & FSLCstrip & 0.83 \(\pm\) 0.11 & 0.81 \(\pm\) 0.11 & 0.85 \(\pm\) 0.22 & 0.83 \(\pm\) 0.09 \\ MLP & Eversense & 0.86 \(\pm\) 0.10 & 0.85 \(\pm\) 0.15 & 0.88 \(\pm\) 0.16 & 0.86 \(\pm\) 0.10 \\ MLP & FSL & 0.80 \(\pm\) 0.12 & 0.76 \(\pm\) 0.19 & 0.85 \(\pm\) 0.22 & 0.79 \(\pm\) 0.13 \\ \hline RF & FSLCstrip & 0.83 \(\pm\) 0.12 & 0.76 \(\pm\) 0.21 & 0.91 \(\pm\) 0.06 & 0.80 \(\pm\) 0.15 \\ RF & Eversense & 0.86 \(\pm\) 0.09 & 0.82 \(\pm\) 0.20 & 0.91 \(\pm\) 0.11 & 0.84 \(\pm\) 0.13 \\ RF & FSL & 0.78 \(\pm\) 0.14 & 0.70 \(\pm\) 0.25 & 0.85 \(\pm\) 0.22 & 0.74 \(\pm\) 0.19 \\ \hline SVM & FSLCstrip & 0.83 \(\pm\) 0.06 & 0.79 \(\pm\) 0.19 & 0.88 \(\pm\) 0.16 & 0.82 \(\pm\) 0.08 \\ SVM & Eversense & 0.88 \(\pm\) 0.08 & 0.85 \(\pm\) 0.15 & 0.91 \(\pm\) 0.17 & 0.87 \(\pm\) 0.09 \\ SVM & FSL & 0.82 \(\pm\) 0.12 & 0.79 \(\pm\) 0.19 & 0.85 \(\pm\) 0.22 & 0.81 \(\pm\) 0.13 \\ \hline TabPFN & FSLCstrip & 0.82 \(\pm\) 0.06 & 0.81 \(\pm\) 0.14 & 0.82 \(\pm\) 0.20 & 0.82 \(\pm\) 0.05 \\ TabPFN & Eversense & 0.91 \(\pm\) 0.10 & 0.91 \(\pm\) 0.17 & **0.91 \(\pm\) 0.17** & 0.91 \(\pm\) 0.11 \\ TabPFN & FSL & 0.77 \(\pm\) 0.11 & 0.73 \(\pm\) 0.24 & 0.82 \(\pm\) 0.20 & 0.74 \(\pm\) 0.16 \\ \hline XGB & FSLCstrip & 0.79 \(\pm\) 0.07 & 0.76 \(\pm\) 0.19 & 0.82 \(\pm\) 0.20 & 0.77 \(\pm\) 0.08 \\ XGB & Eversense & 0.92 \(\pm\) 0.04 & 0.90 \(\pm\) 0.07 & 0.93 \(\pm\) 0.07 & **0.92 \(\pm\) 0.04** \\ XGB & FSL & 0.77 \(\pm\) 0.05 & 0.70 \(\pm\) 0.20 & 0.84 \(\pm\) 0.13 & 0.74 \(\pm\) 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean and standard deviation of classification metrics when considering test subset partitions and the selected features for FSLCstrip, Eversense, and FSL. The best results for each figure of merit are in bold. Table 4 shows the mean and standard deviation of the classification metrics for the five test subset partitions by using only the features relative to each 6-h time interval. As shown, there is a substantial decrease in the predictive performance compared to the results obtained considering features from all time periods (see values in Table 3). This is remarkable in the cases of the afternoon, evening, and night (\(A_{intt}\), \(E_{intt}\), \(N_{intt}\)), where accuracy values are the lowest, in some cases below 0.52, indicating that the models randomly assign classes to the samples. Furthermore, the better time interval to predict if an individual performed high-intensity PA was (\(M_{intt}\)).