# Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features
## 4 Experimental Results and Analysis
### Effectiveness Analysis of Deep Spatiotemporal Feature Module  
To verify the effectiveness of the module proposed in this paper, we compared the B-Stream and J-Stream in the 2s-AGCN with the multi-stream adaptive graph convolution network proposed in this paper. To control the same variables, we did not sample the action sequence. We compared the spatial and temporal features, respectively, and verified the effectiveness of the module proposed in this paper by controlling different inputs. The comparison results are shown in Table 2. We used A to represent an angle, L to represent length, LV to represent linear velocity, AV to represent angular velocity, LA to represent linear acceleration, and AA to represent angular acceleration. The results presented in Table 2 show that the accuracy of the NTU-RGB+D and Kinetics datasets was improved after adding the deep spatiotemporal features. It can be seen that the deep spatiotemporal features module was more effective in improving the accuracy of the model. In the CV index of the NTU-RGB+D dataset, the use of 'LV + AV + LA + AA' as the input feature has the best accuracy, reaching 93.8%, and the effects of other features on this evaluation index were not very different. On the CS index, the use of 'A + L' as the input features achieved the best effect, reaching 87.6%, 1.3% ahead of 'J-Stream'; 'A + L' also performed well on the Kinetics dataset, leading 'J-Stream' by 0.9%. In terms of model training speed, it can be seen in Figure 10 that although the number of features is increased in our proposed method, there is no obvious disadvantage in speed. The original network we used was 2S-AGCN [6], and the model's training method was also consistent with that of the basic network. The turning point of the training curve in Figures 11-16 was caused by the decay of the learning rate. At the beginning of the training process, using a large learning rate (0.1) can accelerate the model convergence. [PERSON] et al. [5] and [PERSON] et al. [6] have proven the effectiveness of learning rate decay in training motion recognition models. With the progress of the training process, the learning rate will be gradually reduced to help find the optimal solution and reduce the fluctuation of the loss \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Cross-View\%** & **Cross-Subject\%** & **Kinetics\%** \\ \hline B-Stream & 93.3 & 86.7 & 34.3 \\ J-Stream & 93.1 & 86.3 & 34.0 \\ \hline A & 93.5 & 87.5 & 32.8 \\ A + L & 93.7 & 87.6 & 34.9 \\ LV + AV & 93.2 & 86.4 & 33.2 \\ LV + AV + LA + AA & 93.8 & 86.5 & 34.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the accuracy of 4-stream features extracted using the spatiotemporal feature extraction module under the same time sampling conditions and the accuracy of the dual-stream features of the original 2s-AGCN. function. Our experiments show that the use of learning rate decay in the training process has a certain effect in the case of our proposed model. In the process of model training, we recorded the loss value and accuracy of each epoch. In Figures 11-16, the red line represents the 'B-Stream', the green line represents the 'J-Stream', the blue line represents the input feature 'A', the green line represents the input feature 'A + L', the purple line represents the input feature 'LV + AV', and the black line represents 'LV + AV + LA + AA'. As can be seen in Figure 10, after adding the deep spatiotemporal feature extraction module, the effect of the model has been further improved based on the original two streams' adaptive graph convolution. As can be seen in Figures 11-14, on the NTU-RGB+D dataset, the model with six branches begins to converge after the 32 nd epoch, the loss value of the converged model is less than 0.1, and the fluctuation is small. Furthermore, the accuracy gradually tends to be stable after 32 epochs.