  A skeleton representation of the human body has been proven to be effective
for this task. The skeletons are presented in graphs form-like. However, the
topology of a graph is not structured like Euclidean-based data. Therefore, a
new set of methods to perform the convolution operation upon the skeleton graph
is presented. Our proposal is based upon the ST-GCN framework proposed by Yan
et al. [1]. In this study, we present an improved set of label mapping methods
for the ST-GCN framework. We introduce three split processes (full distance
split, connection split, and index split) as an alternative approach for the
convolution operation. To evaluate the performance, the experiments presented
in this study have been trained using two benchmark datasets: NTU-RGB+D and
Kinetics. Our results indicate that all of our split processes outperform the
previous partition strategies and are more stable during training without using
the edge importance weighting additional training parameter. Therefore, our
proposal can provide a more realistic solution for real-time applications
centred on daily living recognition systems activities for indoor environments.
