The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control
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Citations
Cites
A new strategy for multifunction myoelectric control
Hudgins, B., Parker, P., & Scott, R. N. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.204774 · Cited by 2,149
Self-citationA 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-citationA wavelet-based continuous classification scheme for multifunction myoelectric control
Englehart, K., Hudgin, B., & Parker, P. A. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.914793 · Cited by 697
Self-citationClassification of the myoelectric signal using time-frequency based representations
Englehart, K., Hudgins, B., Parker, P. A., & Stevenson, M. (1999) · Medical Engineering & Physics · doi:10.1016/s1350-4533(99)00066-1 · Cited by 645
Self-citationHuang, Y., Englehart, K. B., Hudgins, B., & Chan, A. D. C. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.856295 · Cited by 643
Self-citationA Comparison of Surface and Intramuscular Myoelectric Signal Classification
Hargrove, L. J., Englehart, K., & Hudgins, B. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.889192 · Cited by 518
Self-citationA human-assisting manipulator teleoperated by EMG signals and arm motions
Fukuda, O., Tsuji, T., Kaneko, M., & Otsuka, A. (2003) · IEEE Transactions on Robotics and Automation · doi:10.1109/tra.2003.808873 · Cited by 483
A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control
Ajiboye, A. B., & Weir, R. F. (2005) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2005.847357 · Cited by 415
Fuzzy EMG classification for prosthesis control
Chan, F. H. Y., Yang, Y.-S., Lam, F. K., Zhang, Y.-T., & Parker, P. A. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.867872 · Cited by 376
Chu, J.-U., Moon, I., & Mun, M.-S. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.883695 · Cited by 352
Continuous Myoelectric Control for Powered Prostheses Using Hidden Markov Models
Chan, A. D. C., & Englehart, K. B. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.836492 · Cited by 343
Self-citationDecoding 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 1×
“Pattern recognition based myoelectric control systems operate on the assumption that at a given set of electrode locations, the set of features describing the myoelectric signals will be repeatable for a given state of muscle activation and will be different from one state of activation to another [1].”
BackgroundMyoelectric Control Development Toolbox
Chan, A. D. C., & Green, G. C. (2007) · CMBES Proceedings · Cited by 152
The effect of electrode displacements on pattern recognition based myoelectric control
Hargrove, L. J., Englehart, K. B., & Hudgins, B. (2006) · Conference proceedings · doi:10.1109/iembs.2006.260681 · Cited by 139
Self-citationREAL-TIME MYOELECTRIC CONTROL IN A VIRTUAL ENVIRONMENT TO RELATE USABILITY VS. ACCURACY
Lock, B. A., Englehart, K. B., & Hudgins, B. (2005) · DukeSpace (Duke University) · Cited by 102
Self-citationApplying Genetic Programming To Control Of An Artificial Arm
Farry, K. A., Fernandez, J. J., Abramczyk, R., et al. (1997) · DukeSpace (Duke University) · Cited by 21
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
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
Du, Y., Jin, W., Wei, W., Hu, Y., & Geng, W. (2017) · Sensors · doi:10.3390/s17030458 · Cited by 363
Young, A. J., Hargrove, L. J., & Kuiken, T. A. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2159216 · Cited by 287
Self-citationA survey of sensor fusion methods in wearable robotics
Novak, D., & Riener, R. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.08.012 · Cited by 252
Hargrove, L. J., Miller, L. A., Turner, K., & Kuiken, T. A. (2017) · Scientific Reports · doi:10.1038/s41598-017-14386-w · Cited by 190
Self-citation
Metadata
- Authors
- Hargrove, L., Englehart, K., & Hudgins, B.
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2008-04
- Volume
- 3
- Issue
- 2
- Pages
- 175-180
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W2066519883
- Cited by
- 276
- References
- 17
- FWCI
- 4.90
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
