## 5 Discussion
### Ablation Studies
#### 5.1.2 Impact of the ResBiGRU Blocks  
To further evaluate the convolutional block's capacity to collect spatial characteristics, we ran an additional ablation study. We compared our whole model with a modified version that did not include the residual BiGRU block. The revised iteration functioned as the reference point.  
The ablation study examining the impact of the ResBiGRU block on the recognition performance of the CNN-ResBiGRU model is detailed in Table 8, utilizing various sensor modalities from the Myogym dataset. Overall, including the ResBiGRU block consistently enhances the CNN-ResBiGRU model's performance across all sensor modalities compared to the baseline model without it. Specifically, when using accelerometer data, the CNN-ResBiGRU model achieves an F1-score of 88.08% versus 82.10% for the baseline model without the ResBiGRU block. Similarly, for gyroscope data, the CNN-ResBiGRU model achieves an F1-score of 87.02% compared to 83.78% for the baseline model. When incorporating accelerometer and gyroscope data (IMU), the CNN-ResBiGRU model achieves an F1-score of 91.78% compared to 91.49% for the baseline. Using only EMG data significantly improves the F1-score to 74.49% for the CNN-ResBiGRU model, while the baseline model achieves only 54.21%. Combining IMU and EMG data (IMU+EMG) yields an F1-score of 92.68% for the CNN-ResBiGRU model, whereas the model without the ResBiGRU block achieves a slightly lower F1-score of 92.40%.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**F1-score (\%)**} \\ \cline{2-6}  & **Accelerometer** & **Gyroscope** & **IMU** & **EMG** & **IMU + EMG** \\ \hline The proposed model without convolution blocks & 68.90\% & 69.29\% & 80.93\% & 29.92\% & 75.99\% \\ \hline CNN-ResBiGRU & 88.08\% & 87.02\% & 91.78\% & 74.49\% & 92.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Impact of convolutional blocks.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**F1-score (\%)**} \\ \cline{2-6}  & **Accelerometer** & **Gyroscope** & **IMU** & **EMG** & **IMU + EMG** \\ \hline The proposed model without convolution blocks & 82.10\% & 83.78\% & 91.49\% & 54.21\% & 92.40\% \\ \hline CNN-ResBiGRU & 88.08\% & 87.02\% & 91.78\% & 74.49\% & 92.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Impact of ResBiGRU blocks.  
These findings underscore the importance of the ResBiGRU block within the CNN-ResBiGRU model, as it efficiently captures temporal dependencies and long-term contextual information directly from the sensor data. Integrating the ResBiGRU block enhances the model's ability to accurately represent the sequential characteristics of sensor signals, leading to improved identification performance across different contexts. Particularly in EMG data analysis, the CNN-ResBiGRU model demonstrates a significant increase in F1-score compared to the baseline model without the ResBiGRU block, indicating its effectiveness in capturing temporal patterns inherent in EMG data.  
The ablation analysis provides compelling evidence that the ResBiGRU block plays a crucial role in the CNN-ResBiGRU model, contributing significantly to its outstanding implementation in tasks related to recognizing gym exercises across diverse sensor modalities.