  Human Activity Recognition (HAR) is a field of study that focuses on
identifying and classifying human activities. Skeleton-based Human Activity
Recognition has received much attention in recent years, where Graph
Convolutional Network (GCN) based method is widely used and has achieved
remarkable results. However, the representation of skeleton data and the issue
of over-smoothing in GCN still need to be studied. 1). Compared to central
nodes, edge nodes can only aggregate limited neighbor information, and
different edge nodes of the human body are always structurally related.
However, the information from edge nodes is crucial for fine-grained activity
recognition. 2). The Graph Convolutional Network suffers from a significant
over-smoothing issue, causing nodes to become increasingly similar as the
number of network layers increases. Based on these two ideas, we propose a
two-stream graph convolution method called Spatial-Structural GCN (SpSt-GCN).
Spatial GCN performs information aggregation based on the topological structure
of the human body, and structural GCN performs differentiation based on the
similarity of edge node sequences. The spatial connection is fixed, and the
human skeleton naturally maintains this topology regardless of the actions
performed by humans. However, the structural connection is dynamic and depends
on the type of movement the human body is performing. Based on this idea, we
also propose an entirely data-driven structural connection, which greatly
increases flexibility. We evaluate our method on two large-scale datasets,
i.e., NTU RGB+D and NTU RGB+D 120. The proposed method achieves good results
while being efficient.
