Khan, M. Z., Taha, A. , Taylor, W. , Imran, M. A. and Abbasi, Q. H. (2022) Non-invasive localization using software-defined radios. IEEE Sensors Journal, 22(9), pp. 9018-9026. (doi: 10.1109/JSEN.2022.3160796)
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Abstract
Non-invasive indoor human activity detection using radio waves has attracted the interest of researchers, contributing to a range of new applications including smart healthcare. Localisation of activities can assist in developing advanced healthcare systems able to identify the location of patients. Radio frequencies have been shown in numerous studies as a non-invasive method to identify human activity. This is achieved by observing the signal propagation described in the Channel State Information (CSI). This paper presents experimental results using Universal Software-Defined Radio Peripheral (USRP) devices to identify and localise a single human subject performing activities by utilizing the CSI of radio frequencies. The experiments are carried out to retrieve CSI samples observing a single subject perform no-activity, sitting, standing, and leaning forward actions in various positions in a room. Additional CSI is captured for the subject walking in two directions across the observed area. Giving a total of 6 activities spanning the monitored area. CSI is also collected while the monitored area is empty for further comparison. Artificial intelligence is used to make classifications on collected CSI. The proposed approach uses a Super Learner (SL) algorithm that can identify the location of different activities with 96% accuracy, outperforming existing benchmark approaches.
| Item Type: | Articles |
|---|---|
| Additional Information: | This work is supported in parts by EPSRC EP/T021020/1 and EP/T021063/1. Muhammad Zakir Khan’s PhD is funded by BARMT Foreign Scholarship Pakistan. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Taha, Dr Ahmad and Abbasi, Dr Qammer and Khan, Muhammad Zakir and Imran, Professor Muhammad and Taylor, William |
| Authors: | Khan, M. Z., Taha, A., Taylor, W., Imran, M. A., and Abbasi, Q. H. |
| College/School: | College of Science and Engineering College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering |
| Journal Name: | IEEE Sensors Journal |
| Publisher: | IEEE |
| ISSN: | 1530-437X |
| ISSN (Online): | 1558-1748 |
| Published Online: | 21 March 2022 |
| Copyright Holders: | Copyright © 2021 IEEE |
| First Published: | First published in IEEE Sensors Journal 22(9):9018-9026 |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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