Evidence map›Paper›PMID 33921223›Full record

ArticleInternational journal of environmental research and public health2021

An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors.

Hamid Mukhtar, Saeed Rubaiee, Moez Krichen, Roobaea Alroobaea

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
10.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. COVID-19 symptom identification using Deep Learning and hardware emulated systems.Engineering applications of artificial intelligence · 2023
    Article
  5. Review
  6. Smart Hand Sanitisers in the Workplace: A Survey of Attitudes towards an Internet of Things Technology.International journal of environmental research and public health · 2022
    Article
  7. Blockchain for Modern Applications: A Survey.Sensors (Basel, Switzerland) · 2022
    Review
  8. Article
  9. Article
  10. Article
  11. Advances in the Rapid Diagnostic of Viral Respiratory Tract Infections.Frontiers in cellular and infection microbiology · 2022
    Review
  12. Article
  13. Review
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Hamid MukhtarDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.ORCID 0000-0002-9367-4340
Saeed RubaieeDepartment of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah 21577, Saudi Arabia.ORCID 0000-0002-4433-5529
Moez KrichenDepartment of Computer Science, Faculty of Computer Science and Information Technology, Al-Baha University, Al-Baha 65431, Saudi Arabia.ORCID 0000-0001-8873-9755
Roobaea AlroobaeaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.ORCID 0000-0003-1585-2962
Taif University · SAUniversity of Jeddah · SAUniversity of Sfax · TN

Funding

Taif University 1-441-75
6 · The paper itself

Abstract

Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient's critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO2) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient's classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19.

Indexed as

COVID-19Wearable Electronic DevicesHumansMonitoring, PhysiologicSARS-CoV-2algorithmArduinocoronaviruscoughheartbeatIoT

Identifiers

PMID33921223
PMCPMC8070194
OpenAlexW3156134801

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.