Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6890
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sümer, Halil İbrahim | - |
dc.contributor.author | Gürbüz, Sevgi Zübeyde | - |
dc.date.accessioned | 2021-09-11T15:44:06Z | - |
dc.date.available | 2021-09-11T15:44:06Z | - |
dc.date.issued | 2015 | en_US |
dc.identifier.citation | 49th Asilomar Conference on Signals, Systems and Computers -- NOV 08-11, 2015 -- Asilomar, CA | en_US |
dc.identifier.isbn | 978-1-4673-8576-3 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6890 | - |
dc.description.abstract | Falls present a great health threat as people get older, and it has been shown in studies that rapid response is critical to decreasing fall-related mortality. Thus, the development of signal processing algorithms for biomedical applications involving assisted living has become an avid area of research. In this work, a novel algorithm for activity classification and fall detection using a seismic sensor network is proposed. More specifically, classification of falling as well as sources of parasitic signals, such as dropping an object, slamming a door, and shutting a window, are considered. A new target detection and feature extraction algorithm based on wavelet coefficient characterization and spectral statistics is proposed. Results quantifying the performance of the algorithm on real data from a seismic sensor network are given. It is shown that the algorithm offers a reduction of false alarms especially in the case of potentially confusable parasitic signals. | en_US |
dc.description.sponsorship | IEEE Signal Proc Soc | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2015 49Th Asilomar Conference On Signals, Systems And Computers | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | fall detection | en_US |
dc.subject | human activity | en_US |
dc.subject | seismic sensor network | en_US |
dc.subject | classification | en_US |
dc.title | Indoor Fall Detection Using a Network of Seismic Sensors | en_US |
dc.type | Conference Object | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Electrical and Electronics Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | tr_TR |
dc.identifier.startpage | 452 | en_US |
dc.identifier.endpage | 456 | en_US |
dc.identifier.wos | WOS:000380471900083 | en_US |
dc.identifier.scopus | 2-s2.0-84969754367 | en_US |
dc.institutionauthor | Gürbüz, Sevgi Zübeyde | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 49th Asilomar Conference on Signals, Systems and Computers | en_US |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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