# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 5 Experimental Results
### Experimental Comparison and Analysis  
To verify the effectiveness of this study, the proposed optimization algorithm was subjected to ablation experiments. Since human action recognition is a multiclassification task, the cross-entropy loss function was used to train the model. According to the training data, a line chart of the change in loss function value with the iteration number was drawn, as shown in Figure 7.  
On the CS benchmark, the comparison of loss changes obtained by NEW-STGCN-CA and ST-GCN is shown in Figure 7, where both models converge. The NEW-STGCN-CA model converged faster than the ST-GCN model from the start of training until the epoch reaches 10, and the loss value was lower in the same epoch. Then the two curves gradually decreased, and the overall loss value of NEW-STGCN-CA was lower than ST-GCN. First of all, we integrated three different attention mechanisms (SE, CBAM, and CA) into the same position of the ST-GCN and compared them with the experimental results of the original model. According to the experimental results, ST-GCN-CA with the CA attention mechanism has the best experimental results. The experimental results in Table 1 show that the introduction of an attention mechanism can ensure that more discriminant features are assigned higher weights in the calculation process. Figure 7: Change curve of loss values. Adaptive focusing is carried out with different human behaviors to distinguish key nodes and improve the accuracy of recognition. Compared with ST-GCN, NEW-STGCN increases the number of partitions in the sampling region. With the increase in convolution kernels, the network can learn more abundant human joint connections. Therefore, the accuracy of identification can be improved. It can be seen from Table 1 that the accuracy of the NEW-STGCN-CA scheme is improved compared with ST-GCN. With the CS benchmark, the accuracy of the NEW-STGCN-CA algorithm is 3.16% higher than that of the ST-GCN algorithm. On the CV benchmark, NEW-STGCN-CA's accuracy is better by 4.16%. The CV benchmark is the video captured by the 3-angle cameras, which can better extract features and have a high recognition rate. On the CS benchmark, the results show that different recognition objects have great influence on recognition. On the NTU RGB+D 60 dataset, we compared the recognition effect of the NEW-STGCN-CA with several typical methods to validate the feasibility of this research method. First of all, using the classic two-stream model, our accuracy on the CS benchmark is 1.66% higher than two-stream's and 3.16% higher on the CV benchmark. The Clip+CNN+MTLN model is improved based on CNN. Compared with the NEW-STGCN-CA, the accuracy is improved by 5.26% on the CS benchmark and 7.66% on the CV benchmark. On the NTU-RGB+D dataset, the NEW-STGCN-CA model is 3.06% higher in the CS benchmark than the improved ARRN-LSTM model based on RNN and 4.46% higher in the CV benchmark. The accuracy of NEW-STGCN-CA on the CS and CV benchmarks is 0.36% and 1.36% higher than that of BPLHM, respectively. The performance of NEW-STGCN-CA on NTU-RGB+D is 1.36% better than that of CA-GCN on the CS benchmark and 1.06% better than that of the CV benchmark. Similarly, on the Kinetics-Skeleton dataset, we compared the recognition effect of NEW-STGCN-CA with four typical methods, namely ST-GCN, feature Enc based on feature coding, and Deep LSTM and Temporary ConvNet based on deep learning. The experimental results are shown in Table 2. NEW-STGCN-CA is superior to these four typical methods in two evaluation indicators. The accuracy of Top-1 and Top-5 in NEW-STGCN-CA reached 32.40% and 54.80%, respectively. Compared with ST-GCN, the Top-1 accuracy and Top-5 accuracy of NEW-STGCN-CA are 1.7% and 2% higher, respectively. Compared with Feature Enc, Top-1 accuracy and Top-5 accuracy have improved by 17.5% and 29%, respectively.