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REVIEW article

Front. Neurorobot.
Volume 18 - 2024 | doi: 10.3389/fnbot.2024.1383089
This article is part of the Research Topic NeuroDesign in Human-Robot Interaction: The making of engaging HRI technology your brain can’t resist View all 3 articles

Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots

Provisionally accepted
Artur Pilacinski Artur Pilacinski 1*Lukas Christ Lukas Christ 1Marius Boshoff Marius Boshoff 1Ioannis Iossifidis Ioannis Iossifidis 2Patrick Adler Patrick Adler 1Michael Miro Michael Miro 1Bernd Kuhlenkötter Bernd Kuhlenkötter 1Christian Klaes Christian Klaes 1
  • 1 Ruhr University Bochum, Bochum, Germany
  • 2 Ruhr West University of Applied Sciences, Mülheim, Germany

The final, formatted version of the article will be published soon.

    Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.

    Keywords: Human-robot collaboration, brain-machine interfaces, Human action recognition, Sensor Fusion, EEG

    Received: 06 Feb 2024; Accepted: 03 Sep 2024.

    Copyright: © 2024 Pilacinski, Christ, Boshoff, Iossifidis, Adler, Miro, Kuhlenkötter and Klaes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Artur Pilacinski, Ruhr University Bochum, Bochum, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.