A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/82973 |
Resumo: | Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in <i>Scopus</i> and <i>Web of Science</i> databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons. |
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A review on locomotion mode recognition and prediction when using active orthoses and exoskeletonsGait rehabilitationLocomotion mode recognition and predictionWearable assistive devicesScience & TechnologyUnderstanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in <i>Scopus</i> and <i>Web of Science</i> databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.This work was funded in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under grant 2020.05711.BD, under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND, and in part by the FEDER Funds through the COMPETE 2020— Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoMoreira, LuísFigueiredo, JoanaCerqueira, JoãoSantos, Cristina P.2022-09-202022-09-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/82973engMoreira, L.; Figueiredo, J.; Cerqueira, J.; Santos, C.P. A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons. Sensors 2022, 22, 7109. https://doi.org/10.3390/s221971091424-82201424-822010.3390/s22197109362362047109https://www.mdpi.com/1424-8220/22/19/7109info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-30T01:23:53Zoai:repositorium.sdum.uminho.pt:1822/82973Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:58:08.538346Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons |
title |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons |
spellingShingle |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons Moreira, Luís Gait rehabilitation Locomotion mode recognition and prediction Wearable assistive devices Science & Technology |
title_short |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons |
title_full |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons |
title_fullStr |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons |
title_full_unstemmed |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons |
title_sort |
A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons |
author |
Moreira, Luís |
author_facet |
Moreira, Luís Figueiredo, Joana Cerqueira, João Santos, Cristina P. |
author_role |
author |
author2 |
Figueiredo, Joana Cerqueira, João Santos, Cristina P. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Moreira, Luís Figueiredo, Joana Cerqueira, João Santos, Cristina P. |
dc.subject.por.fl_str_mv |
Gait rehabilitation Locomotion mode recognition and prediction Wearable assistive devices Science & Technology |
topic |
Gait rehabilitation Locomotion mode recognition and prediction Wearable assistive devices Science & Technology |
description |
Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in <i>Scopus</i> and <i>Web of Science</i> databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-09-20 2022-09-20T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/82973 |
url |
https://hdl.handle.net/1822/82973 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Moreira, L.; Figueiredo, J.; Cerqueira, J.; Santos, C.P. A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons. Sensors 2022, 22, 7109. https://doi.org/10.3390/s22197109 1424-8220 1424-8220 10.3390/s22197109 36236204 7109 https://www.mdpi.com/1424-8220/22/19/7109 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132371243499520 |