  Spatiotemporal graph convolutional networks (STGCNs) have emerged as a
desirable model for skeleton-based human action recognition. Despite achieving
state-of-the-art performance, there is a limited understanding of the
representations learned by these models, which hinders their application in
critical and real-world settings. While layerwise analysis of CNN models has
been studied in the literature, to the best of our knowledge, there exists no
study on the layerwise explainability of the embeddings learned on
spatiotemporal data using STGCNs. In this paper, we first propose to use a
local Dataset Graph (DS-Graph) obtained from the feature representation of
input data at each layer to develop an understanding of the layer-wise
embedding geometry of the STGCN. To do so, we develop a window-based dynamic
time warping (DTW) method to compute the distance between data sequences with
varying temporal lengths. To validate our findings, we have developed a
layer-specific Spatiotemporal Graph Gradient-weighted Class Activation Mapping
(L-STG-GradCAM) technique tailored for spatiotemporal data. This approach
enables us to visually analyze and interpret each layer within the STGCN
network. We characterize the functions learned by each layer of the STGCN using
the label smoothness of the representation and visualize them using our
L-STG-GradCAM approach. Our proposed method is generic and can yield valuable
insights for STGCN architectures in different applications. However, this paper
focuses on the human activity recognition task as a representative application.
Our experiments show that STGCN models learn representations that capture
general human motion in their initial layers while discriminating different
actions only in later layers. This justifies experimental observations showing
that fine-tuning deeper layers works well for transfer between related tasks.
