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Title: Feature diverse hierarchical classification of human gait with CW radar for assisted living
Authors: Seyfioğlu, Mehmet Saygın
Serinöz, A.
Özbayoğlu, Ahmet Murat
Gürbüz, Sevgi Zübeyde
Keywords: Radar
Feature extraction
scattering centers
Issue Date: 26-Oct-2017
Publisher: Institution of Engineering and Technology
Source: Seyfioğlu, M. S., Serinöz, A., Özbayoğlu, M., & Gürbüz, S. Z. (2017). Feature diverse hierarchical classification of human gait with CW radar for assisted living.
Series/Report no.: IET Conference Publications
Abstract: Activity recognition and estimation of gait parameter are medically essential components of remote health monitoring systems that can improve quality of life, enable personalized treatments, acquire continual medical data to better inform doctors of the patient's well-being, reduce health costs, and ensure rapid response to medical emergencies. Discriminating between a large number of oftentimes similar activities using the radar micro-Doppler effect, however, requires extraction of features that can capture differences in nuances within the signatures. This optimal feature set varies according to the number and type of classes involved. Thus, this work proposes a novel feature diverse hierarchical classification structure, which prevents significant sources of confusion between classes. Our results show a 19% reduction in confusion between creeping and crawling and an elimination of confusion between falling and walking, yielding an overall 7.3% performance improvement above a multi-class support vector machine classifier. © 2017 Institution of Engineering and Technology. All rights reserved.
Description: International Conference on Radar Systems (2017 : United Kingdom)
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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