In EDS ansehen

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

Gespeichert in:
Bibliographische Detailangaben
Titel: Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.
Autoren: Pilacinski, Artur, Christ, Lukas, Boshoff, Marius, Iossifidis, Ioannis, Adler, Patrick, Miro, Michael, Kuhlenkötter, Bernd, Klaes, Christian
Quelle: Frontiers in Neurorobotics; 2024, p1-10, 10p
Schlagwörter: HUMAN activity recognition, BRAIN-computer interfaces, INDUSTRIAL robots, TECHNOLOGICAL innovations, HUMAN mechanics
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Neurorobotics is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Biomedical Index
Beschreibung
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:16625218
DOI:10.3389/fnbot.2024.1383089