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dc.contributor.author
Ryser, Franziska
dc.contributor.author
Butzer, Tobias
dc.contributor.author
Held, Jeremia P.
dc.contributor.author
Lambercy, Olivier
dc.contributor.author
Gassert, Roger
dc.date.accessioned
2018-03-23T13:14:47Z
dc.date.available
2018-03-23T13:14:47Z
dc.date.available
2018-03-23T13:10:17Z
dc.date.available
2018-01-15T02:14:15Z
dc.date.available
2018-01-15T06:40:51Z
dc.date.available
2018-03-23T02:02:12Z
dc.date.issued
2017-07
dc.identifier.isbn
9781538622964
en_US
dc.identifier.isbn
978-1-5386-2296-4
en_US
dc.identifier.other
10.1109/icorr.2017.8009316
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/252788
dc.identifier.doi
10.3929/ethz-b-000228392
dc.description.abstract
To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode placement and connection to external computers. This paper presents the framework for a stand-alone, fully wearable and real-time myoelectric intention detection system based on the Myo armband. The hard and software for real-time gesture classification were developed and combined with a routine to train and customize the classifier, leading to a unique ease of use. The system including training of the classifier can be set up within less than one minute. Results demonstrated that: (1) the proposed algorithm can classify five gestures with an accuracy of 98%, (2) the final system can online classify three gestures with an accuracy of 94.3% and, in a preliminary test, (3) classify three gestures from data acquired from mildly to severely impaired stroke survivors with an accuracy of over 78.8%. These results highlight the potential of the presented system for electromyography-based intention detection for stroke survivors and, with the integration of the system into a robotic hand orthosis, the potential for a wearable platform for all day robot-assisted home rehabilitation.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Electromyography
en_US
dc.subject
Myo armband
en_US
dc.subject
real-time gesture classification
en_US
dc.subject
wearable robotic hand orthosis
en_US
dc.title
Fully embedded myoelectric control for a wearable robotic hand orthosis
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2017-08-15
ethz.book.title
2017 International Conference on Rehabilitation Robotics (ICORR)
en_US
ethz.pages.start
615
en_US
ethz.pages.end
621
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.code.ddc
6 - Technology, medicine and applied sciences::621.3 - Electric engineering
en_US
ethz.event
15th IEEE International Conference on Rehabilitation Robotics (ICORR 2017)
en_US
ethz.event.location
London, England
en_US
ethz.event.date
July 17-20, 2017
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03827 - Gassert, Roger / Gassert, Roger
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03827 - Gassert, Roger / Gassert, Roger
en_US
ethz.tag
RELab
en_US
ethz.tag
Hand Exoskeleton
en_US
ethz.date.deposited
2018-01-15T02:14:19Z
ethz.source
FORM
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-03-26T06:29:50Z
ethz.rosetta.lastUpdated
2019-01-02T12:37:57Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/252096
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/228392
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Fully%20embedded%20myoelectric%20control%20for%20a%20wearable%20robotic%20hand%20orthosis&rft.date=2017-07&rft.spage=615&rft.epage=621&rft.au=Ryser,%20Franziska&Butzer,%20Tobias&Held,%20Jeremia%20P.&Lambercy,%20Olivier&Gassert,%20Roger&rft.isbn=9781538622964&978-1-5386-2296-4&rft.genre=proceeding&rft_id=info:doi/9781538622964&info:doi/978-1-5386-2296-4&rft.btitle=2017%20International%20Conference%20on%20Rehabilitation%20Robotics%20(ICORR)
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