Evidence map›Paper›PMID 39459992›Full record

ArticleSensors (Basel, Switzerland)2024

A Novel Wearable Sensor for Measuring Respiration Continuously and in Real Time.

Amjad Ali, Yang Wei, Yomna Elsaboni, Jack Tyson, Harry Akerman, Alexander I R Jackson, Rod Lane, Daniel Spencer, Neil M White

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Amjad AliSmart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.ORCID 0000-0002-2584-4563
Yang WeiSmart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.ORCID 0000-0001-6195-8595
Yomna ElsaboniSmart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
Jack TysonSchool of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
Harry AkermanClinical Care, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK.
Alexander I R JacksonPerioperative and Critical Care Theme, NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK.ORCID 0000-0002-3153-9231
Rod LaneZelemiq Ltd., Salisbury SP5 1EZ, UK.ORCID 0000-0003-2689-5415
Daniel SpencerSchool of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK.ORCID 0000-0003-3442-9924
Neil M WhiteSchool of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK.ORCID 0000-0003-1532-6452

Funding

National Institute for Health and Care Research (NIHR) NIHR202107
6 · The paper itself

Abstract

In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting.

Indexed as

RespirationWearable Electronic DevicesBiosensing TechniquesElectrodesHumansMonitoring, PhysiologicTextileselectronic textilesrespiratory sensorswearable sensors

Identifiers

PMID39459992
PMCPMC11511516

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.