## 5 Conclusions  
This paper proposes a human pose estimation algorithm based on an improved ST-GCN network. From the network model structure, an improved DETR structure is introduced. The OpenPose algorithm module is used to complete the skeletal point extraction frame by frame and replace the previous CNN module, which effectively suppresses the generation of NMS and anchor points. M-ATT is introduced to ensure that the transformer DETR can capture richer feature information. Introducing a residual network in the 9 th ST-GCN unit in the improved ST-GCN avoids possible network degradation during training, and thus improves the model performance. From the training strategy, the problem of unbalanced FSD data categories is solved by the up-sampling strategy in the data processing stage, and the problem of inconsistent FSD data sequences is solved by the segmented random sampling. In addition, the loss function of the regression branch adopts the mean square error loss, the classification loss adopts the cross-entropy loss, and the momentum-based SGD algorithm is used as the optimizer and configured with warmup, mixup, and regularization strategies. The experimental results show that although \(I_{DETR}\), \(I_{MATT}(h=6)\), and \(I_{Res}\) all help to improve the average PCK of the model on the datasets, our proposed method corresponds to the largest average PCK values, with 93.2% and 92.7% on the FSD and MPII datasets, respectively, which is higher than the average PCK of the original ST-GCN method on these two datasets by 1.9% and 1.7%, respectively. The values of the elements on the diagonal of the confusion matrix obtained for these two datasets are the largest, so this method has a very good recognition effect. In addition, the corresponding model computation of this method is about 1.7 GFLOPs and the corresponding MACs is about 6.4 GMACs, which has a better performance compared with the other three methods analyzed in the paper.  
Conceptualization, M.W. and P.S.; methodology, M.W. and P.S.; software, P.S.; validation, P.S.; formal analysis, M.W.; writing--review and editing, P.S. and M.W.; visualization, P.S.; supervision, M.W. and M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (no. 61701388), the Interdisciplinary Foundation of Xi'an University of Architecture and Technology (no. X20220082), and the Natural Science Foundation of Xi'an University of Architecture and Technology (no. ZR21032). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Mean PCK of FSD** & **Mean PCK of MPII** \\ \hline I\({}_{original}\) & 91.3\% & 91.0\% \\ \hline I\({}_{original}\) + I\({}_{DETR}\) & 92.8\% & 92.3\% \\ \hline I\({}_{original}\) + I\({}_{MATT}\) (h = 6) & 92.1\% & 92.0\% \\ \hline I\({}_{original}\) + I\({}_{Res}\) & 91.7\% & 91.4\% \\ \hline Proposed (I\({}_{original}\) + I\({}_{DETR}\) + I\({}_{MATT}\) + I\({}_{Res}\)) & 93.2\% & 92.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Mean PCK of our methods with different settings in the MPII dataset.  
Data Availability Statement: The datasets presented in this study are openly available on the website. MPII available online: [[http://human-pose.mpi-inf.mpg.de/#overview](http://human-pose.mpi-inf.mpg.de/#overview)]([http://human-pose.mpi-inf.mpg.de/#overview](http://human-pose.mpi-inf.mpg.de/#overview)) accessed on 1 March 2023, FSD available online: [[https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/dataset/fsd.md#Download](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/dataset/fsd.md#Download)]([https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/dataset/fsd.md#Download](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/dataset/fsd.md#Download)) accessed on 1 March 2023.