Rehman, M. et al. (2021) Contactless small-scale movement monitoring system using software defined radio for early diagnosis of COVID-19. IEEE Sensors Journal, 21(15), pp. 17180-17188. (doi: 10.1109/JSEN.2021.3077530)
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240710.pdf - Accepted Version 9MB |
Abstract
The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Imran, Professor Muhammad and Abbasi, Professor Qammer |
| Authors: | Rehman, M., Shah, R. A., Khan, M. B., Ali, N. A. A., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Shah, S. A., Yang, X., Alomainy, A., Imran, M. A., and Abbasi, Q. H. |
| College/School: | College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
| Journal Name: | IEEE Sensors Journal |
| Publisher: | IEEE |
| ISSN: | 1530-437X |
| ISSN (Online): | 1558-1748 |
| Published Online: | 04 May 2021 |
| Copyright Holders: | Copyright © 2021 IEEE |
| First Published: | First published in IEEE Sensors Journal 21(15): 17180-17188 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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