## 5 Experiments and Analysis  
This section includes a description of every experiment we do to evaluate the performance of the suggested FGP-3D action selection framework. Here, we evaluate the adaptive FPG-3D against the confusion matrices. In this section, we show a completely anchored method before and after learning to confirm our analysis. In Figure 3, it can be \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline Skeletal data [34] & 88 \\ Deep LSTM [35] & 90.41 \\ Hybrid feature [36] & 91.25 \\ Hybrid descriptors [37] & 91.63 \\ OMKZ [38] & 93.80 \\ CAD [39] & 95.6 \\ FGP-3D & 99.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification accuracy against state-of-the-art approaches on SBU between the proposed recognition framework and several existing methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline CEV and SGM [33] & 99.4 \\ FGP-3D & 99.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._seen that the previously identified classes are now misclassified, with their associations indicated by weights from several frames that are evenly spaced over the feature observed. Our approach, which is based on a feature matrix with a suggested descriptor and explores longer-range relationships, is able to find dependencies in the skeleton for both correctly and incorrectly classified classes (see Figure 3--misclassified classes). Results in Table 1 and Figure 3 quantitatively demonstrate the efficiency of the FGP-3D. Overall, we draw the conclusion that both our technique and FGP-3D-based methods are better for accommodating joint-based motion detection since they contains both fully gated and anchored motion recognition, have various initialization procedures, and include motion mechanisms in the spatial dimension. It becomes clear that fully gated anchored-based dependency matrix learning along with temporal convolutions can be a more general and successful way to learn spatio-temporal dependencies than cutting-edge models for skeleton-based tasks, where the number of nodes (i.e., spatial body joints) is not large. In this part, we analyze the features of the skeletons recovered by FPG-3D on the MSR dataset in terms of confusion matrices to determine the performance within each class. We analyzed the feature matrix derived from Section 3.4. After a classification using the Fully Gated Anchored Technique, FGP-3D, we freeze the fully anchored skeletal joint approach and retrain classifiers on the MSR dataset. Figure 4 demonstrates that our proposed method with fewer parameters is more effective than classification from scratch. The misclassified classes likewise obtained consistent and reliable results: 99.3, 95.8, 97.8, 98.3, and 98.2 percent accuracy. From the confusion matrix, we conclude that the FPG-3D has a significant advantage for all classes of evaluated datasets at the classification and misclassification steps. Training and testing on MSR are generic and can be utilized for extracting features from skeleton sequences, indicating that the model's features are well-trained on MSR and have a high transferability. Figure 3: In order to visualize the performance of a suggested algorithm, a confusion matrix error matrix is displayed for the UTD-MHAD 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. We first demonstrate the viability of our proposed disentangled multi-scale action feature scheme using a variety of state-of-the-art methods. The various pathways of the FGP-3D action descriptor, which indicates adjacency powering and disentangled aggregation of motion information matrices, are used to do this in Table 3. We are currently describing the performance of the KARD dataset within the class.