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Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices
Within the field of robotics and autonomous systems where there is a human in the loop, intent recognition plays an important role. This is especially true for wearable assistive devices used for rehabilitation, particularly post-stroke recovery. This paper reports results on the use of tactile patt...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805773/ https://www.ncbi.nlm.nih.gov/pubmed/33501139 http://dx.doi.org/10.3389/frobt.2019.00124 |
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author | Stefanou, Thekla Chance, Greg Assaf, Tareq Dogramadzi, Sanja |
author_facet | Stefanou, Thekla Chance, Greg Assaf, Tareq Dogramadzi, Sanja |
author_sort | Stefanou, Thekla |
collection | PubMed |
description | Within the field of robotics and autonomous systems where there is a human in the loop, intent recognition plays an important role. This is especially true for wearable assistive devices used for rehabilitation, particularly post-stroke recovery. This paper reports results on the use of tactile patterns to detect weak muscle contractions in the forearm while at the same time associating these patterns with the muscle synergies during different grips. To investigate this concept, a series of experiments with healthy participants were carried out using a tactile arm brace (TAB) on the forearm while performing four different types of grip. The expected force patterns were established by analysing the muscle synergies of the four grip types and the forearm physiology. The results showed that the tactile signatures of the forearm recorded on the TAB align with the anticipated force patterns. Furthermore, a linear separability of the data across all four grip types was identified. Using the TAB data, machine learning algorithms achieved a 99% classification accuracy. The TAB results were highly comparable to a similar commercial intent recognition system based on a surface electromyography (sEMG) sensing. |
format | Online Article Text |
id | pubmed-7805773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78057732021-01-25 Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices Stefanou, Thekla Chance, Greg Assaf, Tareq Dogramadzi, Sanja Front Robot AI Robotics and AI Within the field of robotics and autonomous systems where there is a human in the loop, intent recognition plays an important role. This is especially true for wearable assistive devices used for rehabilitation, particularly post-stroke recovery. This paper reports results on the use of tactile patterns to detect weak muscle contractions in the forearm while at the same time associating these patterns with the muscle synergies during different grips. To investigate this concept, a series of experiments with healthy participants were carried out using a tactile arm brace (TAB) on the forearm while performing four different types of grip. The expected force patterns were established by analysing the muscle synergies of the four grip types and the forearm physiology. The results showed that the tactile signatures of the forearm recorded on the TAB align with the anticipated force patterns. Furthermore, a linear separability of the data across all four grip types was identified. Using the TAB data, machine learning algorithms achieved a 99% classification accuracy. The TAB results were highly comparable to a similar commercial intent recognition system based on a surface electromyography (sEMG) sensing. Frontiers Media S.A. 2019-11-21 /pmc/articles/PMC7805773/ /pubmed/33501139 http://dx.doi.org/10.3389/frobt.2019.00124 Text en Copyright © 2019 Stefanou, Chance, Assaf and Dogramadzi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Stefanou, Thekla Chance, Greg Assaf, Tareq Dogramadzi, Sanja Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
title | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
title_full | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
title_fullStr | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
title_full_unstemmed | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
title_short | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
title_sort | tactile signatures and hand motion intent recognition for wearable assistive devices |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805773/ https://www.ncbi.nlm.nih.gov/pubmed/33501139 http://dx.doi.org/10.3389/frobt.2019.00124 |
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