  Understanding human actions is a crucial problem for service robots. However,
the general trend in Action Recognition is developing and testing these systems
on structured datasets. That's why this work presents a practical
Skeleton-based Action Recognition framework which can be used in realistic
scenarios. Our results show that although non-augmented and non-normalized data
may yield comparable results on the test split of the dataset, it is far from
being useful on another dataset which is a manually collected data.
