## 4 Experiments
### Comparison with the State-of-the-Art Methods  
In Tables 1 and 2, our proposed method is benchmarked against state-of-the-art methods on the NTU RGB+D 60 and NTU RGB+D 120 datasets, respectively. In order to ensure comparability, we employ the multi-stream fusion strategy at the score level, as is common among most compared methods that use various types of features to boost action recognition performance. Specifically, we first conduct experiments using two single-stream strategies employing two different input features: joint coordinates (joint) and the differential values of joints in the same frames (bone), respectively. Then we perform experiments using a 4-stream strategy that leverages 4 kinds of features as input, including the aforementioned joint and bone features, along with two motion features of the joint and bone that are calculated by computing the differential of these features along the time dimension (fusion-4s). To show the compatibility of our MSKFS method, in addition to using CTR-GCN as the backbone of our approach (ours (CTR-GC [9])), we also adopted BlockGCN as the backbone (ours (Block-GC [10])), which learns topological knowledge from the physical connections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Features** & **X-Sub** & **X-View** \\ \hline ST-GCN [1] & Joint & 81.5 & 88.3 \\ SR-TSL [35] & Joint & 78.8 & 88.2 \\ AS-GCN [2] & Joint & 86.8 & 94.2 \\ AGCN [3] & Joint & - & 93.7 \\ MS-G3D [36] & Joint & 89.4 & 95.0 \\ PL-GCN [37] & Joint & 84.0 & 90.5 \\ MST-GCN [38] & Joint & 89.0 & 95.1 \\ Skeletal [8] & Joint & 89.0 & 95.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with the state-of-the-art action recognition methods (accuracy, %) on the NTU RGB+D 60 dataset. The best results for each feature setting and split are indicated in bold. As shown in the tables, our method performs better than the backbone methods on both datasets under all the data split settings, indicating the effectiveness of our method. Note that [9] also conduct data sampling instead of using all frames as input in their implementation. To be specific, this method randomly crops the center portion of input frames to a target size ranging from 50% to 100% of the original length for data augmentation during training and crops the 90% center part of the entire sequence at the test time. For a detailed understanding of the backbone model, please consult Section 4.5. Actually, our method is complementary to most skeleton-based action recognition methods that mainly consider encoding the spatial relationships of action, because we concentrate on the optimization of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Features** & **X-Sub** & **X-View** \\ \hline DualHead-Net [21] & Joint & 90.3 & **96.1** \\ TransSkeleton [22] & Joint & 90.1 & 95.4 \\ FR Head [39] & Joint & 90.3 & 95.3 \\ BlockGCN [10] & Joint & 90.9 & 95.4 \\ Ours (CTR-GC [9]) & Joint & 90.2 & 95.6 \\ Ours (Block-GC [10]) & Joint & **91.5** & **96.1** \\ DGNN [40] & Fusion-4s & 89.9 & 96.1 \\ Shift-GCN [14] & Fusion-4s & 90.7 & 96.5 \\ Dynamic GCN [5] & Fusion-4s & 91.5 & 96.0 \\ MST-GCN [38] & Fusion-4s & 91.5 & 96.6 \\ Skeletal [8] & Fusion-4s & 91.6 & 96.7 \\ CTR-GCN [9] & Fusion-4s & 92.4 & 96.8 \\ DualHead-Net [21] & Fusion-4s & 92.0 & 96.6 \\ Ta-CNN+ [41] & Fusion-4s & 90.7 & 95.1 \\ MCTM-Net [42] & Fusion-4s & 92.8 & 96.8 \\ FR Head [39] & Fusion-4s & 92.8 & 96.8 \\ BlockGCN [10] & Fusion-4s & **93.1** & 97.0 \\ Ours (CTR-GC [9]) & Fusion-4s & 92.7 & 96.9 \\ Ours (Block-GC [10]) & Fusion-4s & **93.1** & **97.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._