# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 2 Related Work  
The current research shows that human action recognition based on skeletons is a topical research issue [10]. It has important applications on many occasions, such as intelligent interaction, video surveillance, medical health, and so on.  
Convolutional neural networks (CNNs) [11; 12; 13; 14; 15; 16] and recurrent neural networks (RNNs) [17; 18; 19; 20] have improved the performance of action recognition algorithms based on skeleton data, which can extract joint point features from skeleton sequences. However, because skeleton data are non-Euclidean structural data [21], such algorithms ignore the crucial information related to nodes, so the overall improvement is limited. [PERSON] et al. [22] proposed a vision-based automated method for surface condition identification of concrete structures. The recognition of concrete structures combines CNN, transfer learning, and decision-level images, and an improved Dempster-Shafer (D-S) algorithm is also designed to improve the accuracy of recognition. [PERSON] et al. [23] put forward P-LSTM, which can regard all bone points as a whole to learn human actions and add confidence gates to reduce the impact of noise information on the human recognition process. [PERSON] et al. [24] studied the application of deep learning technology in the evaluation of the torsional bearing capacity of reinforced concrete beams, established a data-driven model based on a convolution network, and used an improved bird swarm algorithm (IBSA) to optimize the hyperparameters.  
The difficulties that previous algorithms had to deal with have been solved with the emergence of graph neural networks (GNNs) [25]. It recognizes human motion by extracting the associated features between nodes. Compared with other methods, GNN is more competent for action recognition tasks based on skeleton data. At present, the mainstream method based on video and RGB (red, green, blue) images is the two-stream network [26], while the mainstream method based on skeleton data is the graph convolution network (GCN) [27]. GCN is formed by extending the concept of CNN to GNN, which combines the advantages of both to improve the accuracy of the algorithm. The spatial-temporal graph convolutional neural network (ST-GCN) is proposed based on GCN. It can extract spatial-temporal features from spatial and temporal dimensions, greatly improving the accuracy of action recognition. After considering the long-term dependency between joints, [PERSON] et al. [28] proposed to integrate the context information of each vertex and simplify the network structure through context-aware graph evolution (CA-GCN). [PERSON] et al. [29] proposed a self-attention graph pooling method that uses the self-attention mechanism of graph convolution to distinguish the nodes that should be discarded and retained and can use relatively few parameters to learn end-to-end. [PERSON] et al. [30] proposed a module integrating multilayer perceptron (MLP) and GCN, which can automatically determine key points through graph concern expression and learn in a weakly supervised way, saving time and improving the accuracy of recognition. [PERSON] et al. [31] proposed the recurrent graph evolution neural network (REGNN), which can simultaneously predict multiple multivariate time series. [PERSON] et al. [32] proposed a graph-aware transformer (GAT), which can learn different spatial-temporal motion features from skeleton graph sequences in a data-driven manner.