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...
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| Format: | Article |
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MDPI AG
2018
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| Online Access: | http://hdl.handle.net/1721.1/117001 https://orcid.org/0000-0003-3451-8046 https://orcid.org/0000-0002-0119-1617 |
| _version_ | 1826196366304477184 |
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| author | Siu, Ho Chit Arenas, Ana M. Sun, Tingxiao Stirling, Leia A. |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Siu, Ho Chit Arenas, Ana M. Sun, Tingxiao Stirling, Leia A. |
| author_sort | Siu, Ho Chit |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T10:25:46Z |
| format | Article |
| id | mit-1721.1/117001 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:25:46Z |
| publishDate | 2018 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | mit-1721.1/1170012022-09-30T21:04:14Z 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. Institute for Medical Engineering and Science Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Mechanical Engineering Siu, Ho Chit Arenas, Ana M. Sun, Tingxiao Stirling, Leia A. 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. Jeptha H. and Emily V. Wade Fund 2018-07-19T14:37:34Z 2018-07-19T14:37:34Z 2018-02 2018-01 2018-04-11T17:02:47Z Article http://purl.org/eprint/type/JournalArticle 1424-8220 http://hdl.handle.net/1721.1/117001 Siu, Ho Chit, et al. “Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration.” Sensors, vol. 18, no. 2, Feb. 2018, p. 467. © 2018 by the Authors https://orcid.org/0000-0003-3451-8046 https://orcid.org/0000-0002-0119-1617 http://dx.doi.org/10.3390/s18020467 Sensors Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf MDPI AG MDPI |
| spellingShingle | 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 |
| url | http://hdl.handle.net/1721.1/117001 https://orcid.org/0000-0003-3451-8046 https://orcid.org/0000-0002-0119-1617 |
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