Adaptation Strategies for Personalized Gait Neuroprosthetics.

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of t...

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Autores: Koelewijn AD, Audu M, Del-Ama AJ, Colucci A, Font-Llagunes JM, Gogeascoechea A, Hnat SK, Makowski N, Moreno JC, Nandor M, Quinn R, Reichenbach M, Reyes RD, Sartori M, Soekadar S, Triolo RJ, Vermehren M, Wenger C, Yavuz US, Fey D, Beckerle P
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p20519
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20519
Access Level:acceso abierto
Palabra clave:embedded artificial intelligence
neural interface
neuroprosthesis
personalized devices
perspective
resistive random access memory
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spelling Adaptation Strategies for Personalized Gait Neuroprosthetics.Koelewijn ADAudu MDel-Ama AJColucci AFont-Llagunes JMGogeascoechea AHnat SKMakowski NMoreno JCNandor MQuinn RReichenbach MReyes RDSartori MSoekadar STriolo RJVermehren MWenger CYavuz USFey DBeckerle Pembedded artificial intelligenceneural interfaceneuroprosthesispersonalized devicesperspectiveresistive random access memoryPersonalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics. Copyright © 2021 Koelewijn, Audu, del-Ama, Colucci, Font-Llagunes, Gogeascoechea, Hnat, Makowski, Moreno, Nandor, Quinn, Reichenbach, Reyes, Sartori, Soekadar, Triolo, Vermehren, Wenger, Yavuz, Fey and Beckerle.FRONTIERS MEDIA SA2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20519Frontiers in NeuroroboticsISSN: 16625218reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p205192026-05-27T12:37:41Z
dc.title.none.fl_str_mv Adaptation Strategies for Personalized Gait Neuroprosthetics.
title Adaptation Strategies for Personalized Gait Neuroprosthetics.
spellingShingle Adaptation Strategies for Personalized Gait Neuroprosthetics.
Koelewijn AD
embedded artificial intelligence
neural interface
neuroprosthesis
personalized devices
perspective
resistive random access memory
title_short Adaptation Strategies for Personalized Gait Neuroprosthetics.
title_full Adaptation Strategies for Personalized Gait Neuroprosthetics.
title_fullStr Adaptation Strategies for Personalized Gait Neuroprosthetics.
title_full_unstemmed Adaptation Strategies for Personalized Gait Neuroprosthetics.
title_sort Adaptation Strategies for Personalized Gait Neuroprosthetics.
dc.creator.none.fl_str_mv Koelewijn AD
Audu M
Del-Ama AJ
Colucci A
Font-Llagunes JM
Gogeascoechea A
Hnat SK
Makowski N
Moreno JC
Nandor M
Quinn R
Reichenbach M
Reyes RD
Sartori M
Soekadar S
Triolo RJ
Vermehren M
Wenger C
Yavuz US
Fey D
Beckerle P
author Koelewijn AD
author_facet Koelewijn AD
Audu M
Del-Ama AJ
Colucci A
Font-Llagunes JM
Gogeascoechea A
Hnat SK
Makowski N
Moreno JC
Nandor M
Quinn R
Reichenbach M
Reyes RD
Sartori M
Soekadar S
Triolo RJ
Vermehren M
Wenger C
Yavuz US
Fey D
Beckerle P
author_role author
author2 Audu M
Del-Ama AJ
Colucci A
Font-Llagunes JM
Gogeascoechea A
Hnat SK
Makowski N
Moreno JC
Nandor M
Quinn R
Reichenbach M
Reyes RD
Sartori M
Soekadar S
Triolo RJ
Vermehren M
Wenger C
Yavuz US
Fey D
Beckerle P
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv embedded artificial intelligence
neural interface
neuroprosthesis
personalized devices
perspective
resistive random access memory
topic embedded artificial intelligence
neural interface
neuroprosthesis
personalized devices
perspective
resistive random access memory
description Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics. Copyright © 2021 Koelewijn, Audu, del-Ama, Colucci, Font-Llagunes, Gogeascoechea, Hnat, Makowski, Moreno, Nandor, Quinn, Reichenbach, Reyes, Sartori, Soekadar, Triolo, Vermehren, Wenger, Yavuz, Fey and Beckerle.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20519
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20519
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv FRONTIERS MEDIA SA
publisher.none.fl_str_mv FRONTIERS MEDIA SA
dc.source.none.fl_str_mv Frontiers in Neurorobotics
ISSN: 16625218
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
collection r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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