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


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
10 Oct 2024
Historique:
received: 31 07 2024
revised: 04 09 2024
accepted: 07 10 2024
medline: 26 10 2024
pubmed: 26 10 2024
entrez: 26 10 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 39459992
pii: s24206513
doi: 10.3390/s24206513
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Institute for Health and Care Research (NIHR)
ID : NIHR202107

Auteurs

Amjad Ali (A)

Smart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.

Yang Wei (Y)

Smart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.

Yomna Elsaboni (Y)

Smart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.

Jack Tyson (J)

School of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK.

Harry Akerman (H)

Clinical Care, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK.

Alexander I R Jackson (AIR)

Perioperative and Critical Care Theme, NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK.
Integrative Physiology and Critical Illness Group, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK.

Rod Lane (R)

Zelemiq Ltd., Salisbury SP5 1EZ, UK.

Daniel Spencer (D)

School of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK.

Neil M White (NM)

School of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK.

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Classifications MeSH