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dc.contributor.author
Asfour, Mohammed
dc.contributor.author
Menon, Carlo
dc.contributor.author
Jiang, Xianta
dc.date.accessioned
2021-03-02T06:30:04Z
dc.date.available
2021-03-02T03:54:06Z
dc.date.available
2021-03-02T06:30:04Z
dc.date.issued
2021-02
dc.identifier.issn
1424-8220
dc.identifier.other
10.3390/s21041504
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/472323
dc.identifier.doi
10.3929/ethz-b-000472323
dc.description.abstract
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
force myography
en_US
dc.subject
hand gestures recognition
en_US
dc.subject
machine learning
en_US
dc.subject
data pre-processing
en_US
dc.title
A machine learning processing pipeline for reliable hand gesture classification of fmg signals with stochastic variance
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-02-22
ethz.journal.title
Sensors
ethz.journal.volume
21
en_US
ethz.journal.issue
4
en_US
ethz.pages.start
1504
en_US
ethz.size
15 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
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::09715 - Menon, Carlo / Menon, Carlo
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::09715 - Menon, Carlo / Menon, Carlo
en_US
ethz.date.deposited
2021-03-02T03:54:17Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-02T06:30:14Z
ethz.rosetta.lastUpdated
2022-03-29T05:31:42Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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