## 5 Discussion
### Ablation Studies
#### 5.2.4 Interpretablility of the Proposed Model  
We employed Grad-CAM [36] to enhance the interpretability and transparency of the model we developed. This approach enables us to interpret and pinpoint particular areas within the sensor data that substantially influence the forecasts made by the model. In time-series data, Grad-CAM emphasizes significant occurrences in the sequence that affect the network's classification selections. The visualization features a demonstration sequence with a colormap accentuating these significant areas [41]. In this research, we extracted and normalized the activation features produced by Grad-CAM to a scale of 0 to 1. Figures 11 and 12 illustrate the Grad-CAM activations for arbitrarily chosen portions from the UCI-HAR and WISDM datasets utilizing our ConvResBiGRU-SE model. Brighter shades denote elevated activation outcomes, indicating a more significant influence on the forecast.  
As illustrated in Figure 11, Grad-CAM visualizations display distinct activation patterns for various activities for the UCI-HAR dataset. During walking movements (walking, stepping upstairs, and downstairs), the model emphasizes the periodic patterns in accelerometer and gyroscope signals, highlighted by bright regions in the activation maps. For static activities like sitting and standing, the activation patterns are more evenly distributed, indicating that the model recognizes the sustained nature of these postures. The laying activity exhibits minimal activations across the sensor channels, reflecting the low-intensity nature of this static position.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Recognition Performance**} \\ \cline{2-5}  & \multicolumn{2}{c}{**UCI-HAR**} & \multicolumn{2}{c}{**WISDM**} \\ \cline{2-5}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline ConvResBiGRU-SE without the SE mechanism & 98.86\% (\(\pm\)0.54\%) & 98.93\% (\(\pm\)0.51\%) & 98.25\% (\(\pm\)1.14\%) & 97.29\% (\(\pm\)1.90\%) \\ \hline Our ConvResBiGRU-SE with the SE mechanism & 99.18\% (\(\pm\)0.20\%) & 99.24\% (\(\pm\)0.19\%) & 98.78\% (\(\pm\)0.24\%) & 98.09\% (\(\pm\)0.53\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect the SE mechanism.  
Figure 11: Visualization of the Grad-CAM method using UCI-HAR dataset: (**a**) Walking, (**b**) Walking upstairs, (**c**) Walking downstairs, (**d**) Sitting, (**e**) Standing, and (**f**) Laying. The white lines in the figures represent the original raw sensor signals from the accelerometer and gyroscope channels, allowing for direct visual comparison between the model’s activation patterns (shown by the colored heatmap regions) and the underlying sensor data.  
The results from the WISDM dataset (Figure 12) reveal similar interpretability patterns with some unique characteristics specific to the dataset. The jogging activity displays intense activations at regular intervals, reflecting the rhythmic nature of the movement. The activations are more concentrated in specific sensor channels for standing and sitting activities, indicating that the model distinguishes these static postures through subtle differences in sensor orientations.