## 6 Experimental Results and Discussion
### Analysis of Fusion Methods  
To verify the effectiveness of TG2-CAFNet, we conducted ablation experiments and compared different fusion methods. As shown in Figure 10, confusion matrices were \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Parameters** & \multirow{2}{*}{**FLOPs (M)**} & **Inference** & **Accuracy** & **Precision** & \multirow{2}{*}{**Recall (\%)**} & **F1 Score (\%)** \\  & **(M)** & & **Time (ms)** & **(\%)** & **(\%)** & & \\ \hline GhostNetV1 & 3.90 & 188 & 8.70 & 95.99 & 96.08 & 95.99 & 95.98 \\ GhostNetV2 & 4.89 & 211 & 11.4 & 95.22 & 95.66 & 95.22 & 95.24 \\ GhostNetV1-0.5 & 0.77 & 52.7 & 8.70 & 90.74 & 91.00 & 90.74 & 90.78 \\ GhostNetV2-0.5 & 1.59 & 64.1 & 10.8 & 95.68 & 95.77 & 95.68 & 95.70 \\ TG1 Net & 0.10 & 47.1 & 2.79 & 95.37 & 95.44 & 95.37 & 95.37 \\ TG2 Net\(+\) & 0.24 & 68.1 & 4.08 & 95.37 & 95.48 & 95.37 & 95.38 \\ TG2 Net\(-\) & 0.07 & 26.0 & 2.95 & 96.30 & 96.32 & 96.30 & 96.30 \\ TG2 Net & 0.12 & 52.4 & 3.35 & 97.99 & 98.01 & 97.99 & 97.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of feature extraction module structures. V1 refers to the version without DFC attention, and TG1 Net refers to TG2 Net without DFC. TG2 Net\(+\) and TG2 Net\(-\) represent the increased and decreased depth of TG2 Net. The structures of TG2 Net\(+\) and TG2 Net\(-\) are provided in Tables 4 and 5. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Sub-Module**} & **Expansion Layer Channels** & **Output Channels** \\ \hline Conv2D & None & 16 \\ Ghost-Bottleneck2 & 16 & 16 \\ Ghost-Bottleneck2 & 48 & 24 \\ GhostV2-Bottleneck2 & 160 & 80 \\ GhostV2-Bottleneck2+SE & 240 & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: TG2 Net\(-\) structure. used to observe the performance of TG2 Net when the RTM and TDM were individually used as input features. Confusion matrices help identify the error patterns of different models on specific classes. By observing the confusion matrices, we could find out whether certain categories were easily confused, thereby conducting an in-depth analysis of the contribution of the different feature extraction methods to the different categories. From Figure 10, we can see that the recognition performance of the RTM as a feature was not as good as that of the TDM. The RTM did not perform well in distinguishing similar actions; for example, 12.5% of \"walking\" was misclassified as \"running\", 11.1% of \"kicking\" was misclassified as \"punching\", 6.9% of \"bending\" was misclassified as \"squatting\", and 6.9% of \"squatting\" was misclassified as \"standing up\". This is because an RTM only shows the relationship between the target's distance and time, which can only capture the target's macro movement trajectory, but cannot effectively capture subtle differences between similar actions. In contrast, a TDM reflects the Doppler effect of the target's movement over time, capturing both speed information and micro-movement features. Therefore, in Figure 10, the misclassification rates of the TDM are mostly below 6%. Combining an RTM and TDM can take advantage of both the macro-positioning information provided by the RTM and the speed and micro-movement features captured by the TDM, highlighting the necessity of feature fusion. In Figure 11, we compare traditional fusion methods with the proposed fusion method, CAF. As shown in the figure, concatenation fusion performed the worst overall. Although this method did not lose any information, it was prone to overfitting due to the increased dimensions and was insufficient for feature interactions. Addition fusion performed slightly better than concatenation fusion, and due to its involvement in feature interaction, addition fusion is more robust than concatenation. Multiplication fusion achieved a slightly lower overall accuracy compared to CAF but was far less robust than CAF and addition fusion. The CAF fusion method outperformed the traditional fusion methods in both accuracy and robustness.