Affordable Embroidered EMG Electrodes for Myoelectric Control of Prostheses: A Pilot Study.

conventional gel electrodes embroidered EMG electrodes myoelectric prostheses online and offline performance pilot study

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
03 Aug 2021
Historique:
received: 17 06 2021
revised: 30 07 2021
accepted: 31 07 2021
entrez: 10 8 2021
pubmed: 11 8 2021
medline: 12 8 2021
Statut: epublish

Résumé

Commercial myoelectric prostheses are costly to purchase and maintain, making their provision challenging for developing countries. Recent research indicates that embroidered EMG electrodes may provide a more affordable alternative to the sensors used in current prostheses. This pilot study investigates the usability of such electrodes for myoelectric control by comparing online and offline performance against conventional gel electrodes. Offline performance is evaluated through the classification of nine different hand and wrist gestures. Online performance is assessed with a crossover two-degree-of-freedom real-time experiment using Fitts' Law. Two performance metrics (Throughput and Completion Rate) are used to quantify usability. The mean classification accuracy of the nine gestures is approximately 98% for subject-specific models trained on both gel and embroidered electrode offline data from individual subjects, and 97% and 96% for general models trained on gel and embroidered offline data, respectively, from all subjects. Throughput (0.3 bits/s) and completion rate (95-97%) are similar in the online test. Results indicate that embroidered electrodes can achieve similar performance to gel electrodes paving the way for low-cost myoelectric prostheses.

Identifiants

pubmed: 34372482
pii: s21155245
doi: 10.3390/s21155245
pmc: PMC8347069
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Ernest N Kamavuako (EN)

Department of Engineering, King's College London, London WC2R 2LS, UK.
Faculté de Médecine, Université de Kindu, Kindu, DR, Congo.

Mitchell Brown (M)

Department of Engineering, King's College London, London WC2R 2LS, UK.

Xinqi Bao (X)

Department of Engineering, King's College London, London WC2R 2LS, UK.

Ines Chihi (I)

National Engineering School of Bizerta, Carthage University, Tunis 2070, Tunisia.
Department of Engineering (DOE), The Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, 4365 Luxembourg, Luxembourg.

Samuel Pitou (S)

Department of Engineering, King's College London, London WC2R 2LS, UK.

Matthew Howard (M)

Department of Engineering, King's College London, London WC2R 2LS, UK.

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Classifications MeSH