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Cited by 320

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 access
Young, A. J., Smith, L. H., Rouse, E. J., Hargrove, L. J.·2013·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2012.2232293
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Abstract

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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Metadata

Authors
Young, A. J., Smith, L. H., Rouse, E. J., & Hargrove, L. J.
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-05
Volume
60
Issue
5
Pages
1250-1258
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2012.2232293
OpenAlex
W2111135417
Cited by
320
References
29
FWCI
11.68
Type
journal article
Language
en
Open access
green

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