## 1 Introduction  
In recent years, human action recognition technology has shown strong momentum in practical applications, gradually being applied in various fields such as real-time monitoring and video retrieval, robotics and industrial automation, and in healthcare applications (patient monitoring, rehabilitation assistance, etc.). These specific real-world applications, especially those on devices with limited computational resources, pose higher demands on the recognition accuracy and computational efficiency of models. Additionally, collaboration between humans and robots has greatly enhanced industrial automation and production efficiency. Among the key technologies of human-robot collaboration, rapidly and effectively perceiving human behavior information is a challenge [1; 2; 3; 4; 5]. Deep learning techniques for recognizing actions based on skeleton data have garnered considerable interest due to their resilience in adapting to changing environments [6; 7; 8]. Figure 1 shows a visualization of the human skeletal sequence. Skeletal data are typically acquired through human pose estimation algorithms from RGB video sources or directly from depth sensors. Current research indicates that merely depicting skeleton data as a series of vectors that are handled by Recurrent Neural Networks (RNNs), or as 2D/3D forms analyzed by Convolutional Neural Networks (CNNs), fails to capture the intricate spatio-temporal relationships between body joints. Inspired by graph-based methods, many human action recognition methods have been proposed. [PERSON] et al. [9] were the pioneers in applying spatial-temporal Graph Convolutional Networks (ST-GCNs) to address the challenge of action recognition based on skeleton data. Following this work, an increasing number of research studies [10; 11; 12; 13] based on GCN models have been proposed. However, the receptive fields of the spatial graph are usually predefined, although the related methods make the adjacency matrices learnable, their expressive power is still limited by the regular spatial graph structure. Additionally, to learn more discriminative features from skeleton data, the state-of-the-art (SOTA) methods usually use a large number of learnable parameters to enhance the representative capability of the network [14; 15]. This makes the model training process more complex and computationally expensive. Recently, transformer-based methods have shown great potential in capturing long-range dependencies, but they come with a high computational cost. This makes the algorithm challenging to apply in real-world applications. Thus, reducing model complexity continues to be a significant challenge. To make robots quickly and to effectively understand the actions performed by human companions, this paper proposes an efficient hierarchical co-occurrence GCN model. Similar to CNNs, GCN operations can be seen as the aggregation of local features in the spatial domain and the global feature aggregation across channels for skeleton data. Since they are locally aggregated in space, they may fail to capture remote joint interactions involved in actions such as putting on shoes. Inspired by the HCN algorithm [16], we captured the global response of all joints through a hierarchical co-occurrence approach to utilize the correlations between different joints. The output of the convolution is the global response of all input channels, so if each body joint is treated as a channel, the convolution layer can easily learn the co-occurrence of all joints. This leads us to adopt a simple and practical method to adjust the degree of aggregation on demand. First, the spatial local features of each joint are learned through graph convolution operations. After the transpose operation, the joint dimension is placed into the channels of input, aggregating the global features from all joints through CNN operations. Furthermore, we have introduced separable convolution layers, commonly employed in lightweight CNN architectures, to reduce the model parameters.