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Experience: Cross-Technology Radio Respiratory Monitoring Performance Study

Authors:

Peter Hillyard,

Alemayehu Solomon Abrar,

Krishna Sundar,

Christina Porucznik,

Sarah Hatch PollardAuthors Info & Claims

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

Pages 487 - 496

https://doi.org/10.1145/3241539.3241560

Published: 15 October 2018 Publication History

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Abstract

This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.

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Experience: Cross-Technology Radio Respiratory Monitoring Performance Study

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cover image ACM Conferences

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

October 2018

884 pages

ISBN:9781450359030

DOI:10.1145/3241539

General Chairs:
Rajeev Shorey
TCS Innovation Labs, India
,
Rohan Murty
Soroco, USA/India
,
Program Chairs:
Yingying (Jennifer) Chen
WINLAB, Rutgers University, USA
,
Kyle Jamieson
Princeton University, USA

Copyright © 2018 ACM.

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Published: 15 October 2018

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MobiCom '18: The 24th Annual International Conference on Mobile Computing and Networking

October 29 - November 2, 2018

New Delhi, India

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MobiCom '18 Paper Acceptance Rate 42 of 187 submissions, 22%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Jiao WWang JHe YXi XWang FFang DChen X(2024)Eliminating Design Effort: A Reconfigurable Sensing Framework for Chipless, Backscatter TagsIEEE/ACM Transactions on Networking10.1109/TNET.2023.332026332:2(1155-1170)Online publication date: Apr-2024
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Rivadeneira JFernandes JRodrigues ABoavida FSilva J(2024)An Evaluation of Unobtrusive Sensing in a Healthcare Case StudyIEEE Access10.1109/ACCESS.2024.341955512(89405-89417)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3419555
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