The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Principal Components Analysis Preprocessing for Improved Classification Accuracies in Pattern-Recognition-Based Myoelectric Control
Abstract
Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This "tunes" the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly ( p < 0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects.
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Cites
A robust, real-time control scheme for multifunction myoelectric control
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“Pattern-recognitionbased myoelectric control systems operate on the assumption that at a given electrode location, the set of features describing the MES will be repeatable for a given state of muscle activation and will also be different from one state of activation to another [ 7 ].”
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Cited by
Parajuli, 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
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
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Control of prosthetic hands via the peripheral nervous system
Ciancio, A. L., Cordella, F., Barone, R., Romeo, R. A., Bellingegni, A. D., Sacchetti, R., Davalli, A., Di Pino, G., Ranieri, F., Di Lazzaro, V., et al. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00116 · Cited by 131
Lower Limb Wearable Capacitive Sensing and Its Applications to Recognizing Human Gaits
Zheng, E., Chen, B., Wei, K., & Wang, Q. (2013) · Sensors · doi:10.3390/s131013334 · Cited by 26
Metadata
- Authors
- Hargrove, L. J., Li, G., Englehart, K. B., & Hudgins, B. S.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2009-05
- Volume
- 56
- Issue
- 5
- Pages
- 1407-1414
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2120395111
- Cited by
- 211
- References
- 3
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hargrove, L. J., Li, G., Englehart, K. B., & Hudgins, B. S. (2009). Principal Components Analysis Preprocessing for Improved Classification Accuracies in Pattern-Recognition-Based Myoelectric Control. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2008.2008171
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