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Ali A, Wei Y, Elsaboni Y, Tyson J, Akerman H, Jackson AIR, Lane R, Spencer D, White NM. A Novel Wearable Sensor for Measuring Respiration Continuously and in Real Time. SENSORS (BASEL, SWITZERLAND) 2024; 24:6513. [PMID: 39459992 PMCID: PMC11511516 DOI: 10.3390/s24206513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2024] [Revised: 09/04/2024] [Accepted: 10/07/2024] [Indexed: 10/28/2024]
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.
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Affiliation(s)
- Amjad Ali
- Smart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK; (A.A.)
| | - Yang Wei
- Smart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK; (A.A.)
| | - Yomna Elsaboni
- Smart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK; (A.A.)
| | - Jack Tyson
- School of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK (N.M.W.)
| | - Harry Akerman
- Clinical Care, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Alexander I. R. Jackson
- 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
- Zelemiq Ltd., Salisbury SP5 1EZ, UK
| | - Daniel Spencer
- School of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK (N.M.W.)
| | - Neil M. White
- School of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK (N.M.W.)
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A deep-learning approach to assess respiratory effort with a chest-worn accelerometer during sleep. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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