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Praxis: a framework for AI-driven human action recognition in assembly

Author

Listed:
  • Christos Gkournelos

    (University of Patras)

  • Christos Konstantinou

    (University of Patras)

  • Panagiotis Angelakis

    (University of Patras)

  • Eleni Tzavara

    (University of Patras)

  • Sotiris Makris

    (University of Patras)

Abstract

The role of Artificial intelligence in achieving high performance in manufacturing systems has been explored over the years. However, with the increasing number of variants in the factories and the advances in digital technologies new opportunities arise for supporting operators in the factory. The hybrid production systems stipulate the efficient collaboration of the workers with the machines. Human action recognition is a major enabler for intuitive machines and robots to achieve more efficient interaction with workers. This paper discusses a software framework called Praxis, aiming to facilitate the deployment of human action recognition (HAR) in assembly. Praxis is designed to provide a flexible and scalable architecture for implementing human action recognition in assembly lines. The framework has been implemented in a real-world case study originating for showcasing and validating the effectiveness of Praxis in real-life applications. It is deployed in an assembly use case for an air compression production industry. This study highlights the potential of the Praxis framework for promoting efficient human–robot collaboration (HRC) in modern manufacturing environments through HAR.

Suggested Citation

  • Christos Gkournelos & Christos Konstantinou & Panagiotis Angelakis & Eleni Tzavara & Sotiris Makris, 2024. "Praxis: a framework for AI-driven human action recognition in assembly," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3697-3711, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02228-8
    DOI: 10.1007/s10845-023-02228-8
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