## 4 Evaluation of FGP-3D  
This section includes a valuation description of the proposed FGP-3D action selection framework. In the previous qualitative analysis section, we discussed the ablation experiments that led to the network's specific design, provided justification for each component, and assessed the effectiveness of those components. We use three-dimensional skeletal joints based on a single frame and global selection to examine the behavior of each frame selector component separately. On several datasets, we demonstrate the suggested method's generalizability. Finally, we compare the proposed method to existing cutting-edge frame sampling techniques in an untrimmed environment, demonstrating that it still yields better precision. We contrast our comprehensive model with the state-of-the-art in Tables 1-4 (Figure 2). Table 1 has the values UTD-MHAD [18; 19; 20; 21; 22; 23; 24]. In Table 2, cutting-edge methods [25; 26; 27; 28; 29] and state-of-the-art approaches [28; 30; 31; 32; 33] are contrasted in Table 3. The state-of-the-art approaches [34; 35; 36; 37; 38; 39] are contrasted in Table 4. Our technique surpasses all other methods in every evaluation scenario on all four challenge datasets. Notably, our technique is the first to use a joint multi-aspect model architecture to extract complicated regional spatial-temporal correlations and long-range spatial and temporal dependencies from three-dimensional skeleton sequences. The outcomes support the efficacy of our method. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline BHCCCA [18] & 84.6 \\ DCNN [19] & 88.14 \\ MLSL [20] & 88.37 \\ Fusion of Depth [21] & 89.2 \\ Sensing Fusion [22] & 92.8 \\ End-to-end CNN-LSTM [23] & 98.5 \\ M\({}^{2}\)[24] & 99.2 \\ FGP-3D & 99.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of classification accuracy against state-of-the-art approaches on UTD-MHAD between the proposed recognition framework and several existing methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline CNN + LSTM [25] & 63.10 \\ DTR-HAR [26] & 91.56 \\ D-STIP + D-DESC [27] & 92.00 \\ R-Transform + Zernike [28] & 94.88 \\ DMM [29] & 96.50 \\ FGP-3D & 99.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification accuracy against state-of-the-art approaches on MSR between the proposed recognition framework and several existing methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline R-Transform + Zernike [28] & 96.64 \\ Sensing [30] & 97.22 \\ ASD-R [31] & 97.6 \\ SNSP [32] & 97.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification accuracy against state-of-the-art approaches on KARD between the proposed recognition framework and several existing methods. Table 1 shows that using the FGP-3D network architectures improved accuracy using the UTD-MHAD dataset [40]. Testing and training on the dataset effectively increased the overall classification accuracy from 99.2% to 99.8% when using FGP-3D as the backbone network. This shows the value of pre-training on a sizable dataset for joint recognition. Performance is improved by 0.6% because of the local, global, spatial, and temporal combination classifier that mixes the representations from the two methods. The findings demonstrate that FGP-3D has a better capacity for learning about spatiotemporal representations for action recognition. Our tests are conducted on the challenging action recognition dataset MSR [41] in order to compare our results against FGP-3D. To confirm the usefulness of the suggested model components based on their recognition performance, we conduct thorough ablation studies on them. The completed model is then assessed using cutting-edge techniques for the skeleton-based action recognition challenge. Last but not least, our suggested strategy combines the four main skeletal motion aspects to attain better accuracy in the dataset. Table 2 displays a comparison of joint accuracy using the dataset.