## 4 Experimental Environments and Findings
### Experimental Findings  
#### 4.2.1 Experimental Results from UCI-HAR Dataset  
The experimental findings on the UCI-HAR dataset highlight the exceptional interpretation of our proposed ConvResBiGRU-SE model compared to baseline methods. These results are presented in Table 3.  
Table 3 demonstrates that the model attained an accuracy of \(99.18\%\left(\pm 0.20\%\right)\), markedly exceeding all baseline models. The model's comprehensive performance indicators reveal a precision of \(99.25\%\left(\pm 0.17\%\right)\), underscoring its effectiveness in reducing false positives. The recall rate of \(99.23\%\left(\pm 0.20\%\right)\) illustrates its efficacy in accurately determining true positives. The F1-score of \(99.24\%\left(\pm 0.19\%\right)\) signifies a well-balanced efficiency in precision and recall.  
Among the baseline models, BiGRU performed second best with an accuracy of \(98.77\%\), showcasing the benefits of bidirectional temporal information processing. GRU and CNN also performed well, with accuracies of \(98.27\%\) and \(98.02\%\) respectively. The basic LSTM  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Dataset** & **Walking** & \
\begin{tabular}{c} **Walking** \\ **Upstairs** \\ \end{tabular} &
\begin{tabular}{c} **Walking** \\ **Downstairs** \\ \end{tabular} & **Sitting** & **Standing** & **Laying** & **Jogging** & **Total** \\ \hline UCI-HAR & 1722 & 1544 & 1406 & 1777 & 1906 & 1944 & - & 10,299 \\ WISDM & 4148 & 1236 & 1001 & 597 & 484 & - & 3366 & 10,864 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numbers of segmented samples in each activity class of the UCI-HAR and WISDM datasets.  
Figure 8: Schematic diagram of Stratified 5-fold cross-validation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recognition Performance (Mean\% (ststd.\%))**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1-Score**} \\ \hline CNN & 98.02\% (\(\pm\)0.21\%) & 98.11\% (\(\pm\)0.21\%) & 98.13\% (\(\pm\)0.20\%) & 98.12\% (\(\pm\)0.21\%) \\ LSTM & 96.75\% (\(\pm\)0.74\%) & 96.97\% (\(\pm\)0.67\%) & 96.95\% (\(\pm\)0.71\%) & 96.95\% (\(\pm\)0.70\%) \\ BiLSTM & 97.86\% (\(\pm\)0.64\%) & 98.05\% (\(\pm\)0.54\%) & 97.98\% (\(\pm\)0.62\%) & 97.99\% (\(\pm\)0.61\%) \\ GRU & 98.27\% (\(\pm\)0.61\%) & 98.37\% (\(\pm\)0.58\%) & 98.37\% (\(\pm\)0.56\%) & 98.36\% (\(\pm\)0.57\%) \\ BiGRU & 98.77\% (\(\pm\)0.26\%) & 98.85\% (\(\pm\)0.22\%) & 98.83\% (\(\pm\)0.24\%) & 98.84\% (\(\pm\)0.24\%) \\
**ConvResBiGRU-SE** & **99.18\% (\(\pm\)0.20\%)** & **99.25\% (\(\pm\)0.17\%)** & **99.23\% (\(\pm\)0.20\%)** & **99.24\% (\(\pm\)0.19\%)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of deep learning models on the UCI-HAR dataset (results are presented as mean percentage \(\pm\) standard deviation over 5-fold cross-validation, with top-performing results highlighted in bold).  
model had a relatively lower performance at 96.75%, while BiLSTM improved upon this with an accuracy of 97.86%.  
The low standard deviations (around \(\pm\)0.2%) across all metrics for our proposed model indicate its stability and reliability. Integrating the SE mechanism with the residual architecture enhanced the model's feature learning capability, leading to more accurate and robust activity recognition. These results validate our approach of combining attention mechanisms with deep residual learning for HAR tasks.