Please use this identifier to cite or link to this item: 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
Issue Date: 2015
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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