Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1999
Title: Deep Learning of Micro-Doppler Features for Aided and Unaided Gait Recognition
Authors: Seyfioğlu, Mehmet Saygın
Gürbüz, Sevgi Zübeyde
Özbayoğlu, Ahmet Murat
Yüksel, Melda
Yüksel, Melda/207309
Keywords: micro-Doppler classification
radar
deep learning
Issue Date: 2017
Publisher: IEEE
Source: Seyfioğlu, 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.
Series/Report no.: 2017 IEEE RADAR CONFERENCE (RADARCONF)
Abstract: Remote 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.
Description: IEEE Radar Conference (RadarConf) (2017 : Seattle, WA)
URI: https://ieeexplore.ieee.org/document/7944373
https://hdl.handle.net/20.500.11851/1999
ISBN: 978-1-4673-8823-8
ISSN: 1097-5764
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 full item record

CORE Recommender

SCOPUSTM   
Citations

43
checked on Sep 23, 2022

Page view(s)

24
checked on Nov 28, 2022

Google ScholarTM

Check

Altmetric


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