# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
###### Abstract  
Obtaining discriminative joint features is crucial for skeleton-based human action recognition. Current models mainly focus on the research of skeleton topology encoding. However, their predefined topology is the same and fixed for all action samples, making it challenging to obtain discriminative joint features. Although some studies have considered the complex non-natural connection relationships between joints, the existing methods cannot fully capture this complexity by using high-order adjacency matrices or adding trainable parameters and instead increase the computation parameters. Therefore, this study constructs a novel adaptive channel-enhanced graph convolution (ACE-GCN) model for human action recognition. The model generates similar and affinity attention maps by encoding channel attention in the input features. These maps are complementarily applied to the input feature map and graph topology, which can realize the refinement of joint features and construct an adaptive and non-shared channel-based adjacency matrix. This method of constructing the adjacency matrix improves the model's capacity to capture intricate non-natural connections between joints, prevents the accumulation of unnecessary information, and minimizes the number of computational parameters. In addition, integrating the Edgeconv module into a multi-branch aggregation improves the model's ability to aggregate different scale and temporal features. Ultimately, comprehensive experiments were carried out on NTU-RGB+D 60 and NTU-RGB+D 120, which are two substantial datasets. On the NTU RGB+D 60 dataset, the accuracy of human action recognition was 92% (X-Sub) and 96.3% (X-View). The model achieved an accuracy of 96.6% on the NW-UCLA dataset. The experimental results confirm that the ACE-GCN exhibits superior recognition accuracy and lower computing complexity compared to current methodologies. human action recognition; adaptive graph convolutional neural network; channel attention; skeleton data +
Footnote †: journal: Applied Sciences  
0000-0001-9900-0001-9900]X.X.Han 00000-0001-9900-0001-9900-9000]X.X.Y.Chen 00000-0001-9900-9000-9900-9900-9000-9000-9000-9000-9000-90000-9000-90000-90000-90000-9000-90000-90000-9000-90000-9000-90000-90000-90000-9000-90000-90000-90000-90000-9000-9 and is robust [3]. Due to the progress in sensor technology and the increasing availability of large-scale datasets, there has been significant interest in skeleton-based human action recognition. This approach is valued for its ability to remain effective even in the presence of environmental variations. Because skeleton sequences are sequences of graph data rather than pseudo-image and vector sequences, which are grid-like structures, traditional deep learning networks such as CNN and RNN struggle to fully represent and utilize the natural structural information from human bones [4]. In order to overcome the limitation of grid-like structural features, graph convolutional networks (GCNs) have the ability to extend convolutional neural networks to graphs with diverse architectures. Thus, in the context of skeleton-based human action recognition tasks, GCNs possess a superiority compared to the other two deep learning-based approaches. [PERSON] et al. [5] were the pioneers in employing graph convolutional neural networks for skeleton action identification. They developed the spatial temporal graph convolutional network (ST-GCN). The ST-GCN divides the input human skeleton data into different partitions, assigns different weight labels, and then uses graph convolution to a weighted average or splices its features with those of neighboring nodes to obtain the aggregated features. The model's feature extraction method effectively improves the accuracy of human action recognition tasks on skeleton data and also opens up the application of graph convolutional networks in human skeleton action recognition tasks. [PERSON] et al.