Fusing Hand and Body Skeletons for Human Action Recognition in Assembly

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 use...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Aganian, Dustin, Köhler, Mona, Benedict, Stephan, Eisenbach, Markus, Horst-Michael Gross
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.07.2023
Subjects
Assembly
Fingers
Human activity recognition
Robots
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422