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https://hdl.handle.net/20.500.11851/6890
Title: | Indoor Fall Detection Using a Network of Seismic Sensors | Authors: | Sümer, Halil İbrahim Gürbüz, Sevgi Zübeyde |
Keywords: | fall detection human activity seismic sensor network classification |
Publisher: | IEEE | Source: | 49th Asilomar Conference on Signals, Systems and Computers -- NOV 08-11, 2015 -- Asilomar, CA | 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. | URI: | https://hdl.handle.net/20.500.11851/6890 | ISBN: | 978-1-4673-8576-3 |
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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