Cargando…
Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration
Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856190/ https://www.ncbi.nlm.nih.gov/pubmed/29401754 http://dx.doi.org/10.3390/s18020467 |
_version_ | 1783307263585288192 |
---|---|
author | Siu, Ho Chit Arenas, Ana M. Sun, Tingxiao Stirling, Leia A. |
author_facet | Siu, Ho Chit Arenas, Ana M. Sun, Tingxiao Stirling, Leia A. |
author_sort | Siu, Ho Chit |
collection | PubMed |
description | Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. |
format | Online Article Text |
id | pubmed-5856190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58561902018-03-20 Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration Siu, Ho Chit Arenas, Ana M. Sun, Tingxiao Stirling, Leia A. Sensors (Basel) Article Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. MDPI 2018-02-05 /pmc/articles/PMC5856190/ /pubmed/29401754 http://dx.doi.org/10.3390/s18020467 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Siu, Ho Chit Arenas, Ana M. Sun, Tingxiao Stirling, Leia A. Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration |
title | Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration |
title_full | Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration |
title_fullStr | Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration |
title_full_unstemmed | Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration |
title_short | Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration |
title_sort | implementation of a surface electromyography-based upper extremity exoskeleton controller using learning from demonstration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856190/ https://www.ncbi.nlm.nih.gov/pubmed/29401754 http://dx.doi.org/10.3390/s18020467 |
work_keys_str_mv | AT siuhochit implementationofasurfaceelectromyographybasedupperextremityexoskeletoncontrollerusinglearningfromdemonstration AT arenasanam implementationofasurfaceelectromyographybasedupperextremityexoskeletoncontrollerusinglearningfromdemonstration AT suntingxiao implementationofasurfaceelectromyographybasedupperextremityexoskeletoncontrollerusinglearningfromdemonstration AT stirlingleiaa implementationofasurfaceelectromyographybasedupperextremityexoskeletoncontrollerusinglearningfromdemonstration |