Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1999
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSeyfioğlu, Mehmet Saygın-
dc.contributor.authorGürbüz, Sevgi Zübeyde-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorYüksel, Melda-
dc.date.accessioned2019-07-10T14:42:45Z
dc.date.available2019-07-10T14:42:45Z
dc.date.issued2017
dc.identifier.citationSeyfioğlu, M. S., Gürbüz, S. Z., Özbayoğlu, A. M., & Yüksel, M. (2017, May). Deep learning of micro-Doppler features for aided and unaided gait recognition. In 2017 IEEE Radar Conference (RadarConf) (pp. 1125-1130). IEEE.en_US
dc.identifier.isbn978-1-4673-8823-8
dc.identifier.issn1097-5764
dc.identifier.urihttps://ieeexplore.ieee.org/document/7944373-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/1999-
dc.descriptionIEEE Radar Conference (RadarConf) (2017 : Seattle, WA)
dc.description.abstractRemote health monitoring is a topic that has gained increased interest as a way to improve the quality and reduce costs of health care, especially for the elderly. Falling is one of the leading causes for injury and death among the elderly, and gait recognition can be used to detect and monitor neuromuscular diseases as well as emergency events such as heart attack and seizures. In this work, the potential for radar to discriminate a large number of classes of human aided and unaided motion is demonstrated. Deep learning of micro-Doppler features is used with a 3-layer auto-encoder structure to achieve 89% correct classification, a 17% improvement in performance over the benchmark support vector machine classifier supplied with 127 pre-defined features.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectmicro-Doppler classificationen_US
dc.subjectradaren_US
dc.subjectdeep learningen_US
dc.titleDeep Learning of Micro-Doppler Features for Aided and Unaided Gait Recognitionen_US
dc.typeConference Objecten_US
dc.relation.ispartofseries2017 IEEE RADAR CONFERENCE (RADARCONF)en_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.startpage1125
dc.identifier.endpage1130
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000405307600214en_US
dc.identifier.scopus2-s2.0-85021408492en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.institutionauthorYüksel, Melda-
dc.identifier.doi10.1109/RADAR.2017.7944373-
dc.authorwosidM-5343-2014-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
dc.authorscopusid7006176085-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

43
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

40
checked on Dec 14, 2024

Page view(s)

94
checked on Dec 23, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.