Evidence map›Paper›PMID 36156952›Full record

ArticleComputational intelligence and neuroscience2022

Remote Monitoring of COVID-19 Patients Using Multisensor Body Area Network Innovative System.

Israa Al-Barazanchi, Wahidah Hashim, Ammar Ahmed Alkahtani, Haider Rasheed Abdulshaheed, Hassan Muwafaq Gheni, Aparna Murthy, Elika Daghighi, Shihab A Shawkat, Zahraa A Jaaz

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Israa Al-BarazanchiCollege of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia.ORCID https://orcid.org/0000-0002-6798-6295
Wahidah HashimCollege of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia.ORCID https://orcid.org/0000-0002-7661-5146
Ammar Ahmed AlkahtaniInstitute of Sustainable Energy, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia.ORCID https://orcid.org/0000-0003-2067-9519
Haider Rasheed AbdulshaheedComputer Engineering Techniques Department, Baghdad College of Economic Sciences University, Baghdad, Iraq.ORCID https://orcid.org/0000-0003-3963-875X
Hassan Muwafaq GheniComputer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq.ORCID https://orcid.org/0000-0003-0971-0829
Aparna MurthyProfessional Engineers in Ontario, North York, Toronto, Ontario M2N 6K9, Canada.ORCID https://orcid.org/0000-0002-8762-4427
Elika DaghighiTechnical and Vocational University, Tehran, Iran.ORCID https://orcid.org/0000-0001-5265-5488
Shihab A ShawkatUniversity of Samarra, Samarra, Iraq.ORCID https://orcid.org/0000-0002-9529-2151
Zahraa A JaazCollege of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia.ORCID https://orcid.org/0000-0003-0928-679X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients' fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.

Indexed as

Computer Communication NetworksCOVID-19AlgorithmsHumansMembrane ProteinsReproducibility of ResultsWireless TechnologyADTRP protein, humanMembrane Proteins

Identifiers

PMID36156952
PMCPMC9499756

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Registered trials

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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.