The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Classification of Simultaneous Movements Using Surface EMG Pattern Recognition
Open accessAbstract
Advanced upper limb prostheses capable of actuating multiple degrees of freedom (DOFs) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using nonamputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for nonamputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p < 0.05) than a single linear discriminant analysis (LDA) classifier or a parallel approach. For three-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.
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Citations
Cites
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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
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“Simultaneous pattern recognition has been studied recently (Jiang et al., 2013; Ortiz-Catalan et al., 2013; Young et al., 2013).”
BackgroundJiang, 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
Prosthetic Myoelectric Control Strategies: A Clinical Perspective
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A 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
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
Triwiyanto, T., Pawana, I. P. A., & Purnomo, M. H. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2999505 · Cited by 67
Lukyanenko, P., Dewald, H. A., Lambrecht, J., Kirsch, R. F., Tyler, D. J., & Williams, M. R. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00833-3 · Cited by 27
Use of probabilistic weights to enhance linear regression myoelectric control
Smith, L. H., Kuiken, T. A., & Hargrove, L. J. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/6/066030 · Cited by 15
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Metadata
- Authors
- Young, A. J., Smith, L. H., Rouse, E. J., & Hargrove, L. J.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2013-05
- Volume
- 60
- Issue
- 5
- Pages
- 1250-1258
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2111135417
- Cited by
- 320
- References
- 29
- FWCI
- 11.68
- Type
- journal article
- Language
- en
- Open access
- green
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