Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia

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.

Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

“…Gour et al [ 155 ] proposed an ensemble model based on VGG19 and Xception for detecting COVID-19 from CT and X-Ray images by making both binary and multi-class classification. Researchers like Boulila et al [ 110 ] applied AI technology in building a secure system for persistent patient monitoring thanks to the employment of inexpensive wireless devices and a cutting-edge encryption algorithm called chaos-based substitution boxes. To track daily activities and identify natural and unusual breathing rates.…”

“…However, the study has limitations; it considers dataset labels only and does not consider patient outcomes. Boulila et al [ 110 ] Proposed a new COVID-19 patient monitoring method that protects patient privacy in the setting of Saudi Arabia. It was a secure system for persistent patient monitoring thanks to the employment of inexpensive wireless devices and a cutting-edge encryption algorithm called chaos-based substitution boxes.…”