Evaluation of Surface EMG-based Recognition Algorithms for Decoding Hand Movements

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aut.relation.endpage100
aut.relation.journalMedical and Biological Engineering and Computingen_NZ
aut.relation.startpage83
aut.relation.volume58en_NZ
aut.researcherHutcheson, Catherine
dc.contributor.authorAbbaspour, Sen_NZ
dc.contributor.authorLindén, Men_NZ
dc.contributor.authorGholamhosseini, Hen_NZ
dc.contributor.authorNaber, Aen_NZ
dc.contributor.authorOrtiz-Catalan, Men_NZ
dc.date.accessioned2021-03-05T02:57:00Z
dc.date.available2021-03-05T02:57:00Z
dc.date.copyright2020en_NZ
dc.date.issued2020en_NZ
dc.description.abstractMyoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.en_NZ
dc.identifier.citationMedical and Biological Engineering and Computing, 58, 83–100 (2020). https://doi.org/10.1007/s11517-019-02073-z
dc.identifier.doi10.1007/s11517-019-02073-zen_NZ
dc.identifier.issn0140-0118en_NZ
dc.identifier.issn1741-0444en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14035
dc.languageengen_NZ
dc.publisherSpringer Verlagen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007%2Fs11517-019-02073-zen_NZ
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectClassificationen_NZ
dc.subjectDimensionality reductionen_NZ
dc.subjectElectromyographyen_NZ
dc.subjectFeature extractionen_NZ
dc.subjectMyoelectric pattern recognitionen_NZ
dc.titleEvaluation of Surface EMG-based Recognition Algorithms for Decoding Hand Movementsen_NZ
dc.typeJournal Article
pubs.elements-id366458
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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