Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements

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MARC

Abbaspour, S.Harvard Medical School,Massachusetts Gen Hosp, Dept Neurol, Boston, MA 02114 USA.;Harvard Med Sch, Div Sleep Med, Boston, MA 02114 USA. (author)

Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements

Article/chapterEnglish2021

Publisher, publication year, extent ...

2021-08-23
MDPI AG,2021

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LIBRIS-ID:oai:gup.ub.gu.se/307872
https://gup.ub.gu.se/publication/307872URI
https://doi.org/10.3390/s21165677DOI
https://research.chalmers.se/publication/525856URI
https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55822URI

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Language:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

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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.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Medicinteknik hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Ortopedi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Orthopaedics hsv//eng
TEKNIK OCH TEKNOLOGIER Medicinteknik Annan medicinteknik hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Other Medical Engineering hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Inbäddad systemteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Embedded Systems hsv//eng
classification
electromyography
hand movement
pattern recognition
real-time
classification
signals
Chemistry
Engineering
Instruments & Instrumentation
Classification

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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 (author)
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 (author)
GholamHosseini, H.Auckland University of Technology,Auckland Univ Technol, Dept Elect & Elect Engn, Auckland 1010, New Zealand. (author)
Lindén, Maria,1965-Mälardalens högskola,Inbyggda system(Swepub:mdh)mln04 (author)
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)

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