COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks

Abstract

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

“…Similarly, in [101], a YOLOv4-based FMD is presented but lacks a thorough empirical evaluation. Authors in [115] developed a YOLOv4-based FMD and thermal scanning kiosk to (i) ease the classification of individuals wearing or not wearing masks and (ii) measure pedestrians' body temperatures using a temperature sensing unit. In [116], Gola et al have shown the superiority of YOLOv4 versus MobileNetv2, SSD and YOLOv3 in terms of FMD accuracy, whereas in [117] the authors verified the superiority of YOLOv4 over other variations, with a mAP value of 71.69%, and proposed YOLOv4-tiny for limited computational resources environments.…”

Pollution free UV-C radiation to mitigate COVID-19 transmission

“…The germicidal effect of UV-C is already established with several germs and its use in the disinfecting of medical kits were also under process for COVID 19 virus ( Hashem et al, 2019 , Momattin et al, 2019 , Szeto et al, 2020 , Torres et al, 2020 ). Beside this applications, the different designs were also advanced for sanitization of currency, surface, documents, mask and PPE kit etc ( Pandya et al, 2021 , Ravi et al, 2021 , Tiwary et al, 2021 ). However, these developed design technologies are able to decontaminate the assets up to certain extents but few scientific facts must be considered for effective application.…”

Deep Learning for Depression Detection from Textual Data

“…The portrayal of substantial depressive cues from the literature is one of the most challenging tasks. Previous research has discovered that an automated textual analysis of depressive symptoms can be used in, for example, detecting disrespectful or depressing phrases or sentences in conversations or blog postings, as well as sentiment recovery from suicide notes [1,[15][16][17]. The categorization of shared blog posts is critical to examine to save the lives of any desperate individuals.…”