The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
EMG pattern recognition based on artificial intelligence techniques
Abstract
This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.
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Citations
Cites
Physiology and Mathematics of Myoelectric Signals
De Luca, C. J. (1979) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1979.326534 · Cited by 684
Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes
Graupe, D., & Cline, W. K. (1975) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1975.5408479 · Cited by 354
Cited by
Feature reduction and selection for EMG signal classification
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.102 · Cited by 1,542
Myoelectric control systems—A survey
Asghari Oskoei, M., & Hu, H. (2007) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.07.009 · Cited by 1,244
Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses
Li, G., Schultz, A. E., & Kuiken, T. A. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039619 · Cited by 443
Control of Hand Prostheses Using Peripheral Information
Micera, S., Carpaneto, J., & Raspopovic, S. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2085429 · Cited by 403
Evaluation of the forearm EMG signal features for the control of a prosthetic hand
Boostani, R., & Moradi, M. H. (2003) · Physiological Measurement · doi:10.1088/0967-3334/24/2/307 · Cited by 391
Metadata
- Authors
- Park, S.-H., & Lee, S.-P.
- Venue
- IEEE Transactions on Rehabilitation Engineering
- Published
- 1998
- Volume
- 6
- Issue
- 4
- Pages
- 400-405
- ISSN
- 1063-6528 (ISSN-L)
- OpenAlex
- W2139461861
- Cited by
- 197
- References
- 2
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Park, S.-H., & Lee, S.-P. (1998). EMG pattern recognition based on artificial intelligence techniques. IEEE Transactions on Rehabilitation Engineering. https://doi.org/10.1109/86.736154
Data sources
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.
