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Learning EMG control of a robotic hand: towards active prostheses
Open accessAbstract
We introduce a method based on support vector machines which can detect opening and closing actions of the human thumb, index finger, and other fingers recorded via surface EMG only. The method is shown to be robust across sessions and can be used independently of the position of the arm. With these stability criteria, the method is ideally suited for the control of active prosthesis with a high number of active degrees of freedom. The method is successfully demonstrated on a robotic four-finger hand, and can be used to grasp objects
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Citations
Cited by
Tavakoli, M., Benussi, C., Alhais Lopes, P., Osorio, L. B., & de Almeida, A. T. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.07.010 · Cited by 173
Pang, M., Guo, S., Huang, Q., Ishihara, H., & Hirata, H. (2015) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-015-0033-8 · Cited by 53
Metadata
- Authors
- Bitzer, S., & van der Smagt, P.
- Venue
- Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.
- Publisher
- IEEE
- Published
- 2006-07-10
- Pages
- 2819-2823
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2141195300
- Cited by
- 245
- References
- 0
- FWCI
- 17.54
- Type
- conference paper
- Language
- en
- Open access
- green
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- The paper
- Bitzer, S., & van der Smagt, P. (2006). Learning EMG control of a robotic hand: towards active prostheses. Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. https://doi.org/10.1109/robot.2006.1642128
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
