| Autor: |
Artur Pilacinski, Lukas Christ, Marius Boshoff, Ioannis Iossifidis, Patrick Adler, Michael Miro, Bernd Kuhlenkötter, Christian Klaes |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Frontiers in Neurorobotics, Vol 18 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1662-5218 |
| DOI: |
10.3389/fnbot.2024.1383089 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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