Li, H., Shrestha, A., Heidari, H. , Le Kernec, J. and Fioranelli, F. (2019) Activities Recognition and Fall Detection in Continuous Data Streams Using Radar Sensor. In: IEEE MTT-S 2019 International Microwave Biomedical Conference (IMBioC2019), Nanjing, China, 6-8 May 2019, ISBN 9781538673959 (doi: 10.1109/IMBIOC.2019.8777855)
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Abstract
This student paper presents a Quadratic-kernel Support Vector Machine (SVM) based FMCW (Frequency Modulated Continuous Wave) radar system to recognize daily activities and detect fall accidents. Data collected in this work is divided into two different collection modes, namely, snapshots mode (different activities individually collected in isolation) and continuous activity mode (continuous streams of activities collected one after the other). For the continuous activity streams, a sliding window approach with 4s duration and 70% overlapping has achieved 84.7% classification accuracy and subsequent improvement of 2.6% has been proved by using Sequential Forward Selection (SFS) on six participants to identify an optimal feature set. A ‘tracking’ graph has been utilized to verify that the radar system can correctly identify falls as critical events among the other activities.
| Item Type: | Conference Proceedings |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Fioranelli, Dr Francesco and Heidari, Professor Hadi and Le Kernec, Dr Julien and Li, Haobo and Shrestha, Aman |
| Authors: | Li, H., Shrestha, A., Heidari, H., Le Kernec, J., and Fioranelli, F. |
| College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
| ISBN: | 9781538673959 |
| Copyright Holders: | Copyright © 2019 IEEE |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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