## 4 Experiments and Results
### Experimental Results  
Experiments were conducted to evaluate how well the proposed CNN-ResBiGRU model recognized activities and compare its interpretation with other deep learning methods. The results from various deep learning models in recognizing gym exercise activities using only accelerometer data (Scenario I) are detailed in Table 2. The CNN-ResBiGRU model surpasses all others, boasting a peak accuracy of 95.67% and an F1-score of 88.08%, with a loss of 0.17. Coming in second place, the BiGRU model achieves an accuracy of 95.33%, an F1-score of 86.84%, and a loss of 0.29. BiLSTM and GRU perform similarly, with accuracies of 94.22% and 92.94%, F1-score of 84.08% and 80.09%, and losses of 0.32 and 0.35, respectively. The LSTM model attains an accuracy of 92.54%, an F1-score of 78.58%,and a loss of 0.29. Among the deep learning models, CNN demonstrates the lowest performance, achieving an accuracy of 90.08%, an F1-score of 69.60%, and a loss of 0.48. Table 3 showcases the performance of various deep learning models in identifying gym exercise routines using only gyroscope data (Scenario II). The results are summarized below. The CNN-ResBiGRU model achieves an impressive accuracy of 95.58% and an F1-score of 87.02%, accompanied by a loss of 0.20. Notably, this performance mirrors the outcomes observed in the analysis of accelerometer data in Scenario I. The BiGRU model attains a precision of 93.00%, an F1-score of 78.02%, and a loss of 0.54. Similarly, BiLSTM and LSTM demonstrate comparable performance, with 92.15% and 90.96% accuracy, F1-score of 74.88% and 70.82%, and losses of 0.53 and 0.51, respectively. The GRU model achieves a precision rate of 90.89%, an F1-score of 70.84%, and a loss of 0.58. However, CNN exhibits the lowest performance among the deep learning models, with an accuracy of 82.94%, an F1-score of 42.16%, and a loss of 0.95. Table 4 presents the performance of various deep learning models in identifying gym exercise routines using both accelerometer and gyroscope data (Scenario III). The CNN-ResBiGRU model stands out, with an impressive accuracy of 96.96%, an F1-score of 91.78%, and a loss of 0.14. Notably, this performance surpasses Scenarios I and II, where only accelerometer or gyroscope data were utilized. The BiGRU model achieves an accuracy of 95.03%, an F1-score of 85.22%, and a loss of 0.35%. Similarly, BiLSTM and LSTM demonstrate comparable performance, with accuracies of 94.32% and 94.29%, F1-score of 82.82% and 82.92%, and losses of 0.35 and 0.30, respectively. The GRU model exhibits a mean accuracy of 94.15%, an F1-score of 82.60%, and a loss of 0.34. However, CNN displays the lowest performance among the deep learning models, with an accuracy of 89.13%, an F1-score of 64.72%, and a loss of 0.96. The results reveal that combining accelerometer and gyroscope data, commonly known as IMU data, improved the recognition performance of all deep learning models compared to using either type of sensor data alone. The CNN-ResBiGRU model incorporates spatial and temporal features from accelerometer and gyroscope data, leading to the highest accuracy and F1-score. \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 & 90.08\% (\(\pm\)0.44\%) & 0.48 (\(\pm\)0.03) & 69.60\% (\(\pm\)1.30\%) \\ LSTM & 92.54\% (\(\pm\)0.15\%) & 0.29 (\(\pm\)0.01) & 78.58\% (\(\pm\)0.69\%) \\ BiLSTM & 94.22\% (\(\pm\)0.27\%) & 0.32 (\(\pm\)0.01) & 84.08\% (\(\pm\)0.38\%) \\ GRU & 92.94\% (\(\pm\)0.19\%) & 0.35 (\(\pm\)0.01) & 80.09\% (\(\pm\)1.02\%) \\ BiGRU & 95.33\% (\(\pm\)0.27\%) & 0.29 (\(\pm\)0.01) & 86.84\% (\(\pm\)0.68\%) \\ \hline CNN-ResBiGRU & 95.67\% (\(\pm\)0.21\%) & 0.17 (\(\pm\)0.01) & 88.08\% (\(\pm\)0.56\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative evaluation of deep learning models for gym exercise recognition using accelerometer data (Scenario I).