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

Towards a Skeleton-Based Action Recognition For Realistic Scenarios