The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use
Abstract
Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.
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Citations
Cites
A robust, real-time control scheme for multifunction myoelectric control
Englehart, K., & Hudgins, B. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.813539 · Cited by 1,743
Self-citationCortical control of a prosthetic arm for self-feeding
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008) · Nature · doi:10.1038/nature06996 · Cited by 1,591
EMG feature evaluation for movement control of upper extremity prostheses
Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., & Hashemi, R. M. (1995) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.481972 · Cited by 477
Light, C. M., Chappell, P. H., & Kyberd, P. J. (2002) · Archives of Physical Medicine and Rehabilitation · doi:10.1053/apmr.2002.32737 · Cited by 417
Decoding a New Neural–Machine Interface for Control of Artificial Limbs
Zhou, P., Lowery, M. M., Englehart, K. B., Huang, H., Li, G., Hargrove, L., Dewald, J. P. A., & Kuiken, T. A. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00178.2007 · Cited by 215
Self-citationGraupe, D., Salahi, J., & Kohn, K. H. (1982) · Journal of Biomedical Engineering · doi:10.1016/0141-5425(82)90021-8 · Cited by 205
Cited by
Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., & Aszmann, O. C. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2305111 · Cited by 992
Atzori, M., Cognolato, M., & Müller, H. (2016) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2016.00009 · Cited by 684
“Pattern recognition algorithms are used to classify the movement that the subject aims to perform according to a label (Scheme and Englehart, 2011).”
BackgroundParajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T. J., Naik, G. R., Gunawardana, U., et al. (2019) · Sensors · doi:10.3390/s19204596 · Cited by 332
Jiang, N., Rehbaum, H., Vujaklija, I., Graimann, B., & Farina, D. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2278411 · Cited by 269
Toward higher-performance bionic limbs for wider clinical use
Farina, D., Vujaklija, I., Brånemark, R., Bull, A. M. J., Dietl, H., Graimann, B., Hargrove, L. J., Hoffmann, K.-P., Huang, H., Ingvarsson, T., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00732-x · Cited by 263
Metadata
- Authors
- Scheme, E., & Englehart, K.
- Venue
- The Journal of Rehabilitation Research and Development
- Published
- 2011
- Volume
- 48
- Issue
- 6
- Pages
- 643
- ISSN
- 0742-3241 (ISSN-L)
- 0748-7711
- OpenAlex
- W2171188488
- Cited by
- 969
- References
- 6
- FWCI
- 20.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Scheme, E., & Englehart, K. (2011). Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. The Journal of Rehabilitation Research and Development. https://doi.org/10.1682/jrrd.2010.09.0177
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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.
