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A Review of Force Myography Research and Development

Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register...

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Detalles Bibliográficos
Autores principales: Xiao, Zhen Gang, Menon, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832981/
https://www.ncbi.nlm.nih.gov/pubmed/31635167
http://dx.doi.org/10.3390/s19204557
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author Xiao, Zhen Gang
Menon, Carlo
author_facet Xiao, Zhen Gang
Menon, Carlo
author_sort Xiao, Zhen Gang
collection PubMed
description Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.
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spelling pubmed-68329812019-11-25 A Review of Force Myography Research and Development Xiao, Zhen Gang Menon, Carlo Sensors (Basel) Review Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement. MDPI 2019-10-20 /pmc/articles/PMC6832981/ /pubmed/31635167 http://dx.doi.org/10.3390/s19204557 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Xiao, Zhen Gang
Menon, Carlo
A Review of Force Myography Research and Development
title A Review of Force Myography Research and Development
title_full A Review of Force Myography Research and Development
title_fullStr A Review of Force Myography Research and Development
title_full_unstemmed A Review of Force Myography Research and Development
title_short A Review of Force Myography Research and Development
title_sort review of force myography research and development
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832981/
https://www.ncbi.nlm.nih.gov/pubmed/31635167
http://dx.doi.org/10.3390/s19204557
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