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https://doi.org/10.3390/engpro...
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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E-Textile Breathing Sensor Using Fully Textile Wearable Antennas

Authors: Mahmoud Wagih; Obaid Malik; Alex S. Weddell; Steve Beeby;

E-Textile Breathing Sensor Using Fully Textile Wearable Antennas

Abstract

E-textile sensor networks enable a variety of applications including pervasive monitoring for distributed healthcare. While commercial wearables can now measure various quantities such as heart rate and activities in a real-time, robust, and pervasive manner, breathing sensors remain an ongoing research challenge. In this paper, the use of wearable antennas for respiration monitoring is investigated based on a low-profile broadband fully textile antenna. It is demonstrated that the antenna, suitable for operation on different substrates and body parts, exhibits over 2 dB wireless gain sensitivity to normal breathing. Unlike recent wearable breathing sensors, the proposed antenna has a very simple structure and does not rely on active mechanical sensing elements or specific materials. A simple peak-detection algorithm is investigated showing a nearly 100% breath detection accuracy in line-of-sight. Based on the experimental results, it can be concluded that e-textile antennas can be utilized as highly accurate sensors for respiration monitoring, without the need for specific sensing elements or materials.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
Green
hybrid