Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Stefanou, Thekla, Chance, Greg, Assaf, Tareq, Dogramadzi, Sanja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
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
_version_ 1783636378076053504
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
work_keys_str_mv AT stefanouthekla tactilesignaturesandhandmotionintentrecognitionforwearableassistivedevices
AT chancegreg tactilesignaturesandhandmotionintentrecognitionforwearableassistivedevices
AT assaftareq tactilesignaturesandhandmotionintentrecognitionforwearableassistivedevices
AT dogramadzisanja tactilesignaturesandhandmotionintentrecognitionforwearableassistivedevices