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Real-Time and Offli...
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Abbaspour, S.Harvard Medical School,Massachusetts Gen Hosp, Dept Neurol, Boston, MA 02114 USA.;Harvard Med Sch, Div Sleep Med, Boston, MA 02114 USA.
(författare)
Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
- Artikel/kapitelEngelska2021
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LIBRIS-ID:oai:gup.ub.gu.se/307872
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https://gup.ub.gu.se/publication/307872URI
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https://doi.org/10.3390/s21165677DOI
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https://research.chalmers.se/publication/525856URI
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https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55822URI
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Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
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Naber, Autumn,1988Chalmers tekniska högskola,Chalmers University of Technology,Ctr Bion & Pain Res, S-43180 Molndal, Sweden.;Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden.(Swepub:cth)naber
(författare)
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Ortiz Catalan, Max Jair,1982Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för ortopedi,Institute of Clinical Sciences, Department of Orthopaedics,Chalmers tekniska högskola,Chalmers University of Technology,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Ctr Bion & Pain Res, S-43180 Molndal, Sweden.;Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden.;Sahlgrens Univ Hosp, Operat Area 3, S-43180 Molndal, Sweden.;Univ Gothenburg, Sahlgrenska Acad, Inst Clin Sci, Dept Orthopaed, S-43180 Molndal, Sweden.(Swepub:cth)maxo
(författare)
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GholamHosseini, H.Auckland University of Technology,Auckland Univ Technol, Dept Elect & Elect Engn, Auckland 1010, New Zealand.
(författare)
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Lindén, Maria,1965-Mälardalens högskola,Inbyggda system(Swepub:mdh)mln04
(författare)
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Harvard Medical SchoolMassachusetts Gen Hosp, Dept Neurol, Boston, MA 02114 USA.;Harvard Med Sch, Div Sleep Med, Boston, MA 02114 USA.
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Sensors: MDPI AG21:161424-8220
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