## 4 Experiments and Results
### Experimental Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recognition Performance**} \\ \cline{2-4}  & **Accuracy** & **Loss** & **F1-score** \\ \hline CNN & 82.94\% (\(\pm\)0.55\%) & 0.95 (\(\pm\)0.09) & 42.16\% (\(\pm\)1.27\%) \\ LSTM & 90.96\% (\(\pm\)0.16\%) & 0.51 (\(\pm\)0.02) & 70.82\% (\(\pm\)0.52\%) \\ BiLSTM & 92.15\% (\(\pm\)0.27\%) & 0.53 (\(\pm\)0.05) & 74.88\% (\(\pm\)1.33\%) \\ GRU & 90.89\% (\(\pm\)0.39\%) & 0.58 (\(\pm\)0.01) & 70.84\% (\(\pm\)1.36\%) \\ BiGRU & 93.00\% (\(\pm\)0.21\%) & 0.54 (\(\pm\)0.02) & 78.02\% (\(\pm\)0.55\%) \\ \hline CNN-ResBiGRU & 95.58\% (\(\pm\)0.18\%) & 0.20 (\(\pm\)0.01) & 87.02\% (\(\pm\)0.64\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative evaluation of deep learning models for gym exercise recognition using gyroscope data (Scenario II). Table 5 displays the effectiveness of various deep learning architectures in identifying gym workout activities solely through EMG data, as depicted in Scenario IV. The outcomes are outlined as follows: The CNN-ResBiGRU model illustrates a remarkable precision of 91.53%, an F1-score of 74.49%, and a calculated loss of 0.34. However, this model's efficacy diminishes notably when contrasted with its performance utilizing an accelerometer, gyroscope, or a fusion of both (IMU data) in Scenarios I, II, and III. In contrast, the LSTM model records an accuracy of 78.05%, an F1-score of 24.06%, and a loss of 1.25. Similarly, the BiLSTM model achieves an accuracy of 77.58%, an F1-score of 22.04%, and a loss of 1.83. The performance of GRU and BiGRU appears to be comparable, as indicated by their respective accuracies of 76.57% and 76.18%, accompanied by F1-score of 18.82% and 16.55% and losses of 1.37 and 2.14, respectively. Among the various examined deep learning architectures, the CNN model is the least effective, with an accuracy of 75.74%, an F1-score of 8.00%, and a recorded loss of 0.93. The results suggest a significant decrease in performance across all deep learning models when EMG data alone are utilized for gym activity detection, compared to using accelerometer, gyroscope, or IMU data. This suggests that relying solely on EMG data might not provide adequate information for accurately identifying gym workouts. Table 6 displays the performance of several deep learning models in identifying gym workout activities using a fusion of IMU ( accelerometer and gyroscope) and EMG data, as outlined in Scenario V. The results are summarized as follows: The CNN-ResBiGRU model achieves the highest accuracy of 97.29% and an F1-score of 92.68%, accompanied by a loss of 0.12. This performance surpasses its previous outcomes across all scenarios, highlighting the benefits of combining IMU and EMG data. Meanwhile, the LSTM model records an accuracy of 91.79%, an F1-score of 73.00%, and a loss value of 0.33. Similarly, the BiLSTM model demonstrates a precision rate of 91.20%, an F1-score of 70.62%, and a loss of 0.50. Comparable performance is observed between GRU and BiGRU, with respective accuracies of 89.71% and 89.93%, F1-score of 64.66% and 65.91%, and losses of 0.46 and 0.65. The CNN model achieves an accuracy of 86.43%, an F1-score of 54.22%, and a recorded loss of 0.61, surpassing its performance in Scenario IV, which relied solely on EMG data. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recognition Performance**} \\ \cline{2-4}  & **Accuracy** & **Loss** & **F1-score** \\ \hline CNN & 89.13\% (\(\pm\)0.42\%) & 0.96 (\(\pm\)0.10) & 64.72\% (\(\pm\)1.19\%) \\ LSTM & 94.29\% (\(\pm\)0.15\%) & 0.30 (\(\pm\)0.02) & 82.92\% (\(\pm\)0.73\%) \\ BiLSTM & 94.32\% (\(\pm\)0.07\%) & 0.35 (\(\pm\)0.01) & 82.82\% (\(\pm\)0.35\%) \\ GRU & 94.15\% (\(\pm\)0.15\%) & 0.34 (\(\pm\)0.02) & 82.60\% (\(\pm\)0.29\%) \\ BiGRU & 95.03\% (\(\pm\)0.27\%) & 0.35 (\(\pm\)0.02) & 85.22\% (\(\pm\)0.80\%) \\ \hline CNN-ResBiGRU & 96.96\% (\(\pm\)0.10\%) & 0.14 (\(\pm\)0.01) & 91.78\% (\(\pm\)0.42\%) \\ \hline \hline \end{tabular}
\end{table}