Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia
Boulila, Wadii and Shah, Syed Aziz and Ahmad, Jawad and Driss, Maha and Ghandorh, Hamza and Alsaeedi, Abdullah and Al-Sarem, Mohammed and Saeed, Faisal (2021) Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia. Electronics, 10 (21). p. 2701. ISSN 2079-9292
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Abstract
The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/electronics10212701 | ||||||
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| Uncontrolled Keywords: | COVID-19 patient monitoring; WiFi sensing for respiratory monitoring; privacy preservation; activities of daily living | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence |
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| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Faisal Saeed | ||||||
| Date Deposited: | 05 Jan 2022 14:10 | ||||||
| Last Modified: | 05 Jan 2022 14:10 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/12587 |
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