Fine-Tuning Myoelectric Control Through Reinforcement Learning in a Game Environment
These results showcase the potential of RL for enhancing the reliability of myoelectric controllers, which is of particular importance for advanced bionic limbs.
Authors
- Rita Laezza (ORCID: https://orcid.org/0000-0003-3791-065X)
- Kilian Freitag (ORCID: https://orcid.org/0000-0002-5961-0781)
- Yiannis Karayiannidis (ORCID: https://orcid.org/0000-0001-5129-342X)
- Jan Zbinden (ORCID: https://orcid.org/0000-0001-9535-3746)
Institutions
- Lund University (SE)
- Chalmers University of Technology (SE)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1109/tbme.2025.3578855
- Citations
- 3
- Primary Topic
- Muscle activation and electromyography studies
- Type
- article
- Field-Weighted Citation Impact
- 2.02