## 5 Discussion
### Performance Analysis  
The performance of our ConvResBiGRU-SE model can be evaluated using quantitative metrics and training dynamics. Figure 9 illustrates that the model maintains stable training behavior on the UCI-HAR and WISDM datasets. For the UCI-HAR dataset (Figure 9a), the model converges quickly within the first 25 epochs. Training and validation accuracy curves \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Recognition Performance (Mean\% (tstd.\%))**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline CNN & 92.59\% (\(\pm\)0.77\%) & 90.67\% (\(\pm\)0.87\%) & 98.43\% (\(\pm\)0.98\%) & 89.90\% (\(\pm\)0.91\%) \\ LSTM & 97.40\% (\(\pm\)0.50\%) & 96.13\% (\(\pm\)0.60\%) & 96.08\% (\(\pm\)1.08\%) & 96.09\% (\(\pm\)0.82\%) \\ BiLSTM & 97.86\% (\(\pm\)0.22\%) & 96.88\% (\(\pm\)0.58\%) & 96.82\% (\(\pm\)0.58\%) & 96.84\% (\(\pm\)0.57\%) \\ GRU & 97.45\% (\(\pm\)0.24\%) & 96.34\% (\(\pm\)0.42\%) & 96.10\% (\(\pm\)0.37\%) & 96.20\% (\(\pm\)0.40\%) \\ BiGRU & 97.96\% (\(\pm\)0.20\%) & 96.96\% (\(\pm\)0.50\%) & 96.70\% (\(\pm\)0.61\%) & 96.82\% (\(\pm\)0.56\%) \\
**ConvResBiGRU-SE** & **98.78\% (\(\pm\)0.24\%)** & **98.04\% (\(\pm\)0.49\%)** & **98.16\% (\(\pm\)0.56\%)** & **98.09\% (\(\pm\)0.53\%)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of deep learning models on the WISDM dataset (results are presented as mean percentage \(\pm\) standard deviation over 5-fold cross-validation, with top-performing results highlighted in bold). show steady improvement before leveling off at approximately 99% accuracy. The loss curves initially drop sharply and stabilize, indicating effective learning without overfitting. Comparable training dynamics are noted for the WISDM dataset (Figure 9), albeit with some distinct features. The model converges around epoch 50, with the final accuracy stabilizing at about 98.7%. The slower convergence rate compared to the UCI-HAR dataset is likely due to the unique challenges of the WISDM dataset, such as its larger size and more varied activity patterns. Our implementation of early stopping proved critical to model performance. While Figure 9 displays the entire 200-epoch trajectory for completeness, the vertical lines indicate where early stopping typically occurs (around epoch 75 for UCI-HAR and 50 for WISDM). This approach effectively prevented overfitting, as evidenced by the consistent accuracy of validation and loss curves following convergence. The difference in convergence speed between the two datasets highlights how our training strategy adapts to dataset complexity, terminating earlier for more straightforward recognition tasks while allowing more epochs for more challenging ones. The confusion matrices in Figure 10 offer deeper insights into the model's classification performance. For the UCI-HAR dataset (Figure 10), the model demonstrates excellent discrimination across all activity classes, with most diagonal elements exceeding 0.98. Notably, the model effectively distinguishes between similar activities like walking and walking upstairs, a common challenge in HAR systems. Figure 9: Accuracy and loss curves of the proposed model using different datasets: (**a**) UCI-HAR (**b**) WISDM. The results for the WISDM dataset (Figure 9(b)) show similarly strong performance, though with slightly more variation across activity classes. Static activities such as sitting and standing are recognized with near-perfect accuracy (>0.99). Dynamic activities have slightly lower but impressive recognition rates (>0.96). The minor confusion between walking and walking upstairs (around 0.02) reflects the inherent similarity in their sensor patterns. The confusion matrices depicted in Figure 10 illustrate normalized values compiled from all five folds of our stratified cross-validation process. Each fold generates a distinct confusion matrix, and these five matrices were aggregated and normalized to create the final visualization.