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Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography

Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surrou...

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Autores principales: Malesevic, Nebojsa, Björkman, Anders, Andersson, Gert S., Cipriani, Christian, Antfolk, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269860/
https://www.ncbi.nlm.nih.gov/pubmed/35808549
http://dx.doi.org/10.3390/s22135054
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author Malesevic, Nebojsa
Björkman, Anders
Andersson, Gert S.
Cipriani, Christian
Antfolk, Christian
author_facet Malesevic, Nebojsa
Björkman, Anders
Andersson, Gert S.
Cipriani, Christian
Antfolk, Christian
author_sort Malesevic, Nebojsa
collection PubMed
description Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database.
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spelling pubmed-92698602022-07-09 Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography Malesevic, Nebojsa Björkman, Anders Andersson, Gert S. Cipriani, Christian Antfolk, Christian Sensors (Basel) Article Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database. MDPI 2022-07-05 /pmc/articles/PMC9269860/ /pubmed/35808549 http://dx.doi.org/10.3390/s22135054 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Malesevic, Nebojsa
Björkman, Anders
Andersson, Gert S.
Cipriani, Christian
Antfolk, Christian
Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
title Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
title_full Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
title_fullStr Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
title_full_unstemmed Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
title_short Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
title_sort evaluation of simple algorithms for proportional control of prosthetic hands using intramuscular electromyography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269860/
https://www.ncbi.nlm.nih.gov/pubmed/35808549
http://dx.doi.org/10.3390/s22135054
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