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Sign language gesture recognition with bispectrum features using SVM

2020Conference object Open access
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dc.contributor.authorAhmed, Hasmath Farhana Thariq
dc.contributor.authorAhmad, Hafisoh
dc.contributor.authorPhang, Swee King
dc.contributor.authorVaithilingam, Chockalingam Aravind
dc.contributor.authorHarkat, Houda
dc.contributor.authorNarasingamurthi, Kulasekharan
dc.contributor.editorPhang, SK
dc.contributor.editorMahdiraji, GA
dc.contributor.editorVaithilingam, CA
dc.date.accessioned2021-06-24T11:36:05Z
dc.date.available2021-06-24T11:36:05Z
dc.date.issued2020
dc.description.abstractWi-Fi based sensing system captures the signal reflections due to human gestures as Channel State Information (CSI) values in subcarrier level for accurately predicting the fine-grained gestures. The proposed work explores the Higher Order Statistical (HOS) method by deriving bispectram features (BF) from raw signal by adopting a Conditional Informative Feature Extraction (CIFE) technique from information theory to form a subset of informative and best features. Support Vector Machine (SVM) classifier is adopted in the present work for classifying the gesture and to measure the prediction accuracy. The present work is validated on a secondary dataset, SignFi, having data collected from two different environments with varying number of users and sign gestures. SVM reports an overall accuracy of 83.8%, 94.1%, 74.9% and 75.6% in different environments/scenarios.
dc.description.sponsorshipTaylor's University through its TAYLOR'S PhD SCHOLARSHIP Programme
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1063/5.0002344
dc.identifier.isbn978-0-7354-1992-6
dc.identifier.issn0094-243X
dc.identifier.urihttp://hdl.handle.net/10400.1/16626
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAmerican Institute of Physics
dc.relation.ispartofseriesAIP Conference Proceedings
dc.subject.otherEngineering
dc.titleSign language gesture recognition with bispectrum features using SVM
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceTaylors Univ Lakeside Campus, Subang Jaya, MALAYSIA
oaire.citation.startPage030001
oaire.citation.title13Th International Engineering Research Conference (13Th Eureca 2019)
oaire.citation.volume2233
person.familyNameHarkat
person.givenNameHouda
person.identifier.orcid0000-0002-7827-1527
rcaap.rightsopenAccess
rcaap.typeconferenceObject
relation.isAuthorOfPublicationff3a322c-945f-465a-b746-c69eab18be72
relation.isAuthorOfPublication.latestForDiscoveryff3a322c-945f-465a-b746-c69eab18be72

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