## 5 Experiments and Analysis
We incrementally add model elements to test the effectiveness of FGP-3D modules in capturing complicated spatial-temporal aspects. The results are displayed in Figure 5. However, if we just replicate the factorized pathway to learn from multiple feature subspaces and imitate the fully gated anchored design or simply scale up the factorized pathway to bigger capacity (deeper and wider), we notice modest gains. Class number 10 is accurately categorized, while two other classes have an accuracy of 99.3 and only a few frames of misclassification. Contrarily, when the FGP-3D pathway is included, we regularly see better results with the same number of parameters or less, demonstrating FGP-3D's capacity to detect intricate regional spatial-temporal correlations that were previously missed by factorizing spatial and temporal dependencies. These findings show the effectiveness of the proposed disentangled aggregation technique for joint-based action learning; it enhances the proposed FGP-3D module's performance in the spatial-temporal domain and across a wide range of number scales. Figure 4: In order to visualize the performance of a suggested algorithm, a confusion matrix error matrix is displayed for the MSR dataset. Each row of the matrix represents cases belonging to an actual class, and each column represents instances belonging to a predicted class, or conversely. There are ten levels of accuracy for the results, and it is evident from the graph that our method has low error due to the small number of misclassified frames. Figure 5: In order to visualize the performance of a suggested algorithm, a confusion matrix error matrix is displayed for the KARD dataset. Each row of the matrix represents cases belonging to an actual class, and each column represents instances belonging to a predicted class, or conversely. There are ten levels of accuracy for the results, and it is evident from the graph that our method has low error due to the small number of misclassified frames. The features of the skeletons recovered by FGP-3D and analyzed using the SBU dataset are shown in Figure 6. The findings in Table 4 show that transfer learning is more successful with fewer criteria than previous research classifications. The accuracy and fine-tuning plots are displayed as a confusion matrix (see Figure 6). According to the figures, which show ten phases of correct and incorrect classification, we can infer that when the proposed action descriptor is employed, there are only 0.5% losses and a significant improvement in accuracy for the two-person mutual action dataset. This indicates that the descriptor weights of the model we have suggested are well-pre-trained on the SBU dataset, offering a strong transfer ability, and can be utilized to extract features from skeletal sequences. Each figure and table should be referenced as Figures 3-6, Tables 1-4, etc., in the body of the document. In this part, we discussed our design method that goes beyond FPG-3D by modeling the relationships between joints in our unified formulation utilizing a general dependency matrix FPG-3D (see Equation (18) and Figure 7) and the fully gated anchored approach to identify skeleton-based action recognition. Figure 6: In order to visualize the performance of a suggested algorithm, a confusion matrix error matrix is displayed for the SBU dataset. Each row of the matrix represents cases belonging to an actual class, and each column represents instances belonging to a predicted class, or conversely. There are ten levels of accuracy for the results, and it is evident from the graph that our method has low error due to the small number of misclassified frames. Figure 7: The proposed FGP-3D models’ predictive abilities evaluate performance in terms of accuracy within the class. Most metrics are built around the idea of percentage predictions made by the model and compared to the actual results.