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Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study

BACKGROUND: A direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstructio...

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Autores principales: Yepes, Juan C., Portela, Mario A., Saldarriaga, Álvaro J., Pérez, Vera Z., Betancur, Manuel J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6318920/
https://www.ncbi.nlm.nih.gov/pubmed/30606192
http://dx.doi.org/10.1186/s12938-018-0622-1
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author Yepes, Juan C.
Portela, Mario A.
Saldarriaga, Álvaro J.
Pérez, Vera Z.
Betancur, Manuel J.
author_facet Yepes, Juan C.
Portela, Mario A.
Saldarriaga, Álvaro J.
Pérez, Vera Z.
Betancur, Manuel J.
author_sort Yepes, Juan C.
collection PubMed
description BACKGROUND: A direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstruction. However, there is a need to perform RAT during other phases of ACL injury rehabilitation before attempting an advanced exercise such as walking. This paper aims to propose a myoelectric control (MEC) algorithm for a robot-assisted rehabilitation system, “Nukawa”, to assist knee movement during these types of exercises, i.e., such as in active-assisted extension exercises. METHODS: Surface electromyography (sEMG) signal processing algorithm was developed to detect the motion intention of the knee joint. The sEMG signal processing algorithm and the movement control algorithm, reported by the authors in a previous publication, were joined together as a hardware-in-the-loop simulation to create and test the MEC algorithm, instead of using the actual robot. EXPERIMENTS AND RESULTS: An experimental protocol was conducted with 17 healthy subjects to acquire sEMG signals and their lower limb kinematics during 12 ACL rehabilitation exercises. The proposed motion intention algorithm detected the orientation of the intention 100% of the times for the extension and flexion exercises. Also, it detected in 94% and 59% of the cases the intensity of the movement intention in a comparable way to the maximum voluntary contraction (MVC) during extension exercises and flexion exercises, respectively. The maximum position mean absolute error was [Formula: see text] , [Formula: see text] , and [Formula: see text] for the hip, knee, and ankle joints, respectively. CONCLUSIONS: The MEC algorithm detected the intensity of the movement intention, approximately, in a comparable way to the MVC and the orientation. Moreover, it requires no prior training or additional torque sensors. Also, it controls the speed of the knee joint of Nukawa to assist the knee movement, i.e., such as in active-assisted extension exercises.
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spelling pubmed-63189202019-01-08 Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study Yepes, Juan C. Portela, Mario A. Saldarriaga, Álvaro J. Pérez, Vera Z. Betancur, Manuel J. Biomed Eng Online Research BACKGROUND: A direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstruction. However, there is a need to perform RAT during other phases of ACL injury rehabilitation before attempting an advanced exercise such as walking. This paper aims to propose a myoelectric control (MEC) algorithm for a robot-assisted rehabilitation system, “Nukawa”, to assist knee movement during these types of exercises, i.e., such as in active-assisted extension exercises. METHODS: Surface electromyography (sEMG) signal processing algorithm was developed to detect the motion intention of the knee joint. The sEMG signal processing algorithm and the movement control algorithm, reported by the authors in a previous publication, were joined together as a hardware-in-the-loop simulation to create and test the MEC algorithm, instead of using the actual robot. EXPERIMENTS AND RESULTS: An experimental protocol was conducted with 17 healthy subjects to acquire sEMG signals and their lower limb kinematics during 12 ACL rehabilitation exercises. The proposed motion intention algorithm detected the orientation of the intention 100% of the times for the extension and flexion exercises. Also, it detected in 94% and 59% of the cases the intensity of the movement intention in a comparable way to the maximum voluntary contraction (MVC) during extension exercises and flexion exercises, respectively. The maximum position mean absolute error was [Formula: see text] , [Formula: see text] , and [Formula: see text] for the hip, knee, and ankle joints, respectively. CONCLUSIONS: The MEC algorithm detected the intensity of the movement intention, approximately, in a comparable way to the MVC and the orientation. Moreover, it requires no prior training or additional torque sensors. Also, it controls the speed of the knee joint of Nukawa to assist the knee movement, i.e., such as in active-assisted extension exercises. BioMed Central 2019-01-03 /pmc/articles/PMC6318920/ /pubmed/30606192 http://dx.doi.org/10.1186/s12938-018-0622-1 Text en © The Author(s) 2019 Open AccessThis 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Yepes, Juan C.
Portela, Mario A.
Saldarriaga, Álvaro J.
Pérez, Vera Z.
Betancur, Manuel J.
Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study
title Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study
title_full Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study
title_fullStr Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study
title_full_unstemmed Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study
title_short Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study
title_sort myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6318920/
https://www.ncbi.nlm.nih.gov/pubmed/30606192
http://dx.doi.org/10.1186/s12938-018-0622-1
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