COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

Vedaei, Seyed Shahim; Fotovvat, Amir; Mohebbian, Mohammad Reza; Rahman, Gazi M. E.; Wahid, Khan A.; Babyn, Paul; Marateb, Hamid Reza; Mansourian, Marjan; Sami, Ramin

doi:10.1109/access.2020.3030194

Cited by 188 publications

(88 citation statements)

References 44 publications

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“…For the diabetic patients suffering from cardiovascular diseases, a fog computing enabled health monitoring system is proposed by Gia et al [26]. Vedaei et al [27] developed a framework COVID-SAFE, to minimize the corona exposure risk. The data processing and analysis is carried out on fog nodes integrated with Machine Learning (ML) tools.…”

Section: Related Workmentioning

confidence: 99%

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

et al. 2021

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Section: Related Workmentioning

confidence: 99%

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

et al. 2021

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“…Penelitian [12] menggunakan IoT untuk perawatan kesehatan dan pemantauan jarak fisik untuk situasi pandemik. Kerangka yang diusulkan terdiri dari tiga bagian yaitu: simpul IoT yang ringan dan berbiaya rendah, aplikasi smart phone dan Machine Learning sebagai alat untuk diagnosis dan analisis.…”

Section: Studi Literaturunclassified

Rancang Bangun Pengukur Suhu Tubuh Dengan Multi Sensor Untuk Mencegah Penyebaran Covid-19

Putra

Budiyanto

2021

RESTI

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During the COVID-19 pandemic, the price of preventive equipment such as masks and hand sanitizers has increased significantly. Likewise, thermometers are experiencing an increase and scarcity, this tool is also sought after by many companies for screening employees and guests before entering the building to detect body temperatures that are suspected of being positive for COVID-19. The use of a thermometer operated by humans is very risky because dealing directly with people who could be ODP (People Under Monitoring/Suscpected ) or even positive for COVID-19, therefore we need tools for automatic body temperature screening and do not involve humans for the examination. This research uses the MLX-90614 body temperature sensor equipped with an ultrasonic support sensor to detect movement and measure the distance between the forehead and the temperature sensor so that the body heat measurement works optimally, and a 16x2 LCD to display the temperature measurement results. If the measured body temperature is more than 37.5 ° C degrees Celsius then the buzzer will turn on and the selenoid door lock will not open and will send a notification to the Telegram messaging application. The final result obtained is the formation of a prototype device for measuring body temperature automatically without the need to involve humans in measuring body temperature to control people who want to enter the building so as to reduce the risk of COVID-19 transmission

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“…In our study, the Khorshid COVID-19 Cohort (KCC) dataset [22,34] weeks, and the first year after discharge. In total, 630 COVID-19 patients were enrolled in our study.…”

Section: Dataset and Variable Selectionmentioning

confidence: 99%

Absolute Mortality Risk Assessment of COVID-19 Patients: The Khorshid COVID Cohort (KCC) Study

Marateb

Cube

Sami

et al. 2021

Preprint

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Background: Already at the time of hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis.Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on following variables which can simply be measured at hospital admission: gender, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead.Results: Cause-specific hazard regression models show that these baseline variables are associated with both hazards, the death as well as the discharge hazard. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC=0.872 [CI 95%: 0.835-0.910]).Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

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COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

Cited by 188 publications

References 44 publications

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

Rancang Bangun Pengukur Suhu Tubuh Dengan Multi Sensor Untuk Mencegah Penyebaran Covid-19

Absolute Mortality Risk Assessment of COVID-19 Patients: The Khorshid COVID Cohort (KCC) Study

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