Fusing Hand and Body Skeletons for Human Action Recognition in Assembly
This work addresses the need for more accurate action recognition in human-robot collaboration for industrial manufacturing, though it is incremental as it builds on existing skeleton-based methods.
The paper tackles the problem of recognizing human actions in assembly tasks by combining body and hand skeletons, achieving improved accuracy for assembly-specific actions.
As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial. Cobots should be able to recognize human actions to assist with assembly tasks and act autonomously. To achieve this, skeleton-based approaches are often used due to their ability to generalize across various people and environments. Although body skeleton approaches are widely used for action recognition, they may not be accurate enough for assembly actions where the worker's fingers and hands play a significant role. To address this limitation, we propose a method in which less detailed body skeletons are combined with highly detailed hand skeletons. We investigate CNNs and transformers, the latter of which are particularly adept at extracting and combining important information from both skeleton types using attention. This paper demonstrates the effectiveness of our proposed approach in enhancing action recognition in assembly scenarios.