Evidence map›Paper›PMID 36071953›Full record

ReviewInternational journal of biomaterials2022

Emerging Development of Auto-Charging Sensors for Respiration Monitoring.

Hamza Abu Owida, Muhammad Al-Ayyad, Jamal I Al-Nabulsi

Abstract readReview
In one paragraph

Review in International journal of biomaterials, 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

3 authors.

Hamza Abu OwidaMedical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan.ORCID https://orcid.org/0000-0001-6943-6134
Muhammad Al-AyyadMedical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan.
Jamal I Al-NabulsiMedical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan.ORCID https://orcid.org/0000-0002-9665-7888

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the development of biomedical monitoring systems, including respiration monitoring systems, has been accelerated. Wearable and implantable medical devices are becoming increasingly important in the diagnosis and management of disease and illness. Respiration can be monitored using a variety of biosensors and systems. Auto-charged sensors have a number of advantages, including low cost, ease of preparation, design flexibility, and a wide range of applications. It is possible to use the auto-charged sensors to directly convert mechanical energy from the airflow into electricity. The ability to monitor and diagnose one's own health is a major goal of auto-charged sensors and systems. Respiratory disease model output signals have not been thoroughly investigated and clearly understood. As a result, figuring out their exact interrelationship is a difficult and important research question. This review summarized recent developments in auto-charged respiratory sensors and systems in terms of their device principle, output property, detecting index, and so on. Researchers with an interest in auto-charged sensors can use the information presented here to better understand the difficulties and opportunities that lie ahead.

Identifiers

PMID36071953
PMCPMC9444417

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.