## 6 Experimental Results and Discussion
### Analysis of Fusion Methods
This is attributed to CAF's nonlinear fusion mechanism, which effectively integrates multiple features, avoids the overfitting problem of concatenation fusion, and surpasses addition and multiplication fusion in feature interactions. Therefore, CAF demonstrated a superior performance in handling complex features. To better understand how CAF performs feature fusion, we visualized the features extracted by TG2-CAFNet in Figure 12, including the RT and TF features before CAF fusion and the features after fusion. We analyzed the visualized features of two actions: running and punching. As shown in the figure, in the case of running, the RT features before CAF fusion covered more dispersed regions, indicated by several low-brightness green blocks, whereas the TF features were more focused on specific key areas. After CAF fusion, the final feature map was primarily dominated by the TF features, with the RT features being diminished. This demonstrates that CAF effectively filters and retains features that are more important for action recognition, thereby enhancing the model's accuracy. Figure 10: Confusion matrices of the two types of spectrograms. (**a**) TG2 Net extracting features solely from the RTM. (**b**) TG2 Net extracting features solely from the TDM. In contrast, for the punching action, both RT and TF features were significant before CAF fusion, and both were well preserved in the fused feature map, indicating a complementary fusion. Hence, CAF is not a simple linear fusion, but rather has an adaptive capability to balance different features. It can selectively fuse features based on their importance for different actions, allowing CAF to achieve a better recognition performance when handling complex actions. Figure 11: Comparison of traditional fusion methods and the proposed CAF method. Figure 12: Feature maps before and after CAF. (**a**) shows the visualization for “walking” action, and (**b**) shows the visualization for “punching” action. To demonstrate the significant advantages of CAF, a statistical significance test was conducted. In this study, a one-way analysis of variance (ANOVA) was first carried out to evaluate the differences between the fusion methods. The results of the ANOVA test showed that the F value was 51.6145 and the \(p\)-value was 2.88 \(\times\) 10\({}^{-21}\), which is much smaller than the significance level of 0.05. This indicates that the differences in accuracy among the different fusion methods were statistically significant, meaning that at least two groups had a significant difference. While the ANOVA test successfully demonstrated that there was a statistically significant difference between the groups, it did not specify which groups exhibited significant differences. Therefore, a post hoc Tukey HSD (honest significant difference) test was performed, as shown in Table 6. The Tukey HSD test indicated that the mean differences between CAF and the other methods were significant, particularly for concatenation, multiplication, and summation. The \(p\)-adj values were close to or equal to 0 (where \(p\)-adj = 0 indicates an extremely small \(p\)-value), and the mean differences (Mean Diff) were relatively large. In contrast, the difference between multiplication and summation was not significant (\(p\)-adj = 0.4044), suggesting that their performance was similar. The Mean Diff in Table 6 actually reflects the average accuracy difference between the two fusion methods. A larger mean difference indicates a more pronounced performance difference between the two groups. The mean differences between CAF and the other three fusion methods were all large, and the confidence intervals did not include zero, indicating that these differences were statistically significant. This aligns with the ANOVA test results, further verifying the significant performance advantage of CAF over the other fusion methods. Finally, in Figure 13, we present a detailed error analysis using two confusion matrices of TG2-CAFNet.