The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Evaluation of the forearm EMG signal features for the control of a prosthetic hand
Open accessAbstract
The purpose of this research is to select the best features to have a high rate of motion classification for controlling an artificial hand. Here, 19 EMG signal features have been taken into account. Some of the features suggested in this study include combining wavelet transform with other signal processing techniques. An assessment is performed with respect to three points of view: (i) classification of motions, (ii) noise tolerance and (iii) calculation complexity. The energy of wavelet coefficients of EMG signals in nine scales, and the cepstrum coefficients were found to produce the best features in these views.
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Cites
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145
Statistical pattern recognition: a review
Jain, A. K., Duin, P. W., & Mao, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824819 · Cited by 6,767
Higher order spectra analysis, a nonlinear signal processing framework
(1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90335-2 · Cited by 1,133
Zero-crossings of a wavelet transform
Mallat, S. (1991) · IEEE Transactions on Information Theory · doi:10.1109/18.86995 · Cited by 704
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
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
Biomedical Signal Processing: Innovation and Applications
(2021) · Springer eBooks · doi:10.1007/978-3-030-67494-6 · Cited by 261
EMG Pattern Analysis and Classification for a Prosthetic Arm
Saridis, G. N., & Gootee, T. P. (1982) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1982.324954 · Cited by 236
The application of neural networks to myoelectric signal analysis: a preliminary study
Kelly, M. F., Parker, P. A., & Scott, R. N. (1990) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.52324 · Cited by 221
EMG pattern recognition based on artificial intelligence techniques
Park, S.-H., & Lee, S.-P. (1998) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.736154 · Cited by 197
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
A Strategy for Identifying Locomotion Modes Using Surface Electromyography
Huang, H., Kuiken, T. A., & Lipschutz, R. D. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2003293 · Cited by 512
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
EMG Pattern Recognition in the Era of Big Data and Deep Learning
Phinyomark, A., & Scheme, E. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2030021 · Cited by 272
Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees
Cipriani, C., Antfolk, C., Controzzi, M., Lundborg, G., Rosen, B., Carrozza, M. C., & Sebelius, F. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2108667 · Cited by 233
Metadata
- Authors
- Boostani, R., & Moradi, M. H.
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2003-03-20
- Volume
- 24
- Issue
- 2
- Pages
- 309-319
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2113998107
- Cited by
- 391
- References
- 17
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- bronze
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