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Regressing grasping using force myography: an exploratory study
BACKGROUND: Partial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable effect on the amputee’s life. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. T...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199756/ https://www.ncbi.nlm.nih.gov/pubmed/30352593 http://dx.doi.org/10.1186/s12938-018-0593-2 |
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author | Sadeghi Chegani, Rana Menon, Carlo |
author_facet | Sadeghi Chegani, Rana Menon, Carlo |
author_sort | Sadeghi Chegani, Rana |
collection | PubMed |
description | BACKGROUND: Partial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable effect on the amputee’s life. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously. METHODS: Ten able-bodied participants performed three hand movements while their wrist was fixed in one of six predefined positions. The angle between Thumb and Index finger ([Formula: see text] ), and Thumb and Middle finger ([Formula: see text] ) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifier that identified the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated. RESULTS: The first approach had a correlation of determination ([Formula: see text] ) of 0.871 for [Formula: see text] and [Formula: see text] . Using the second approach [Formula: see text] and [Formula: see text] were obtained. The first approach is over two times faster than the second approach while having similar performance; thus the first approach was selected to investigate the effect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically significant. A statistically significant decrease in performance resulted when less than five wrist positions were used for training. CONCLUSIONS: The results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12938-018-0593-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6199756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61997562018-10-31 Regressing grasping using force myography: an exploratory study Sadeghi Chegani, Rana Menon, Carlo Biomed Eng Online Research BACKGROUND: Partial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable effect on the amputee’s life. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously. METHODS: Ten able-bodied participants performed three hand movements while their wrist was fixed in one of six predefined positions. The angle between Thumb and Index finger ([Formula: see text] ), and Thumb and Middle finger ([Formula: see text] ) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifier that identified the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated. RESULTS: The first approach had a correlation of determination ([Formula: see text] ) of 0.871 for [Formula: see text] and [Formula: see text] . Using the second approach [Formula: see text] and [Formula: see text] were obtained. The first approach is over two times faster than the second approach while having similar performance; thus the first approach was selected to investigate the effect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically significant. A statistically significant decrease in performance resulted when less than five wrist positions were used for training. CONCLUSIONS: The results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12938-018-0593-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-23 /pmc/articles/PMC6199756/ /pubmed/30352593 http://dx.doi.org/10.1186/s12938-018-0593-2 Text en © The Author(s) 2018 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 Sadeghi Chegani, Rana Menon, Carlo Regressing grasping using force myography: an exploratory study |
title | Regressing grasping using force myography: an exploratory study |
title_full | Regressing grasping using force myography: an exploratory study |
title_fullStr | Regressing grasping using force myography: an exploratory study |
title_full_unstemmed | Regressing grasping using force myography: an exploratory study |
title_short | Regressing grasping using force myography: an exploratory study |
title_sort | regressing grasping using force myography: an exploratory study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199756/ https://www.ncbi.nlm.nih.gov/pubmed/30352593 http://dx.doi.org/10.1186/s12938-018-0593-2 |
work_keys_str_mv | AT sadeghicheganirana regressinggraspingusingforcemyographyanexploratorystudy AT menoncarlo regressinggraspingusingforcemyographyanexploratorystudy |