  Egocentric action recognition is essential for healthcare and assistive
technology that relies on egocentric cameras because it allows for the
automatic and continuous monitoring of activities of daily living (ADLs)
without requiring any conscious effort from the user. This study explores the
feasibility of using 2D hand and object pose information for egocentric action
recognition. While current literature focuses on 3D hand pose information, our
work shows that using 2D skeleton data is a promising approach for hand-based
action classification, might offer privacy enhancement, and could be less
computationally demanding. The study uses a state-of-the-art transformer-based
method to classify sequences and achieves validation results of 94%,
outperforming other existing solutions. The accuracy of the test subset drops
to 76%, indicating the need for further generalization improvement. This
research highlights the potential of 2D hand and object pose information for
action recognition tasks and offers a promising alternative to 3D-based
methods.
