Robust RFID-based Respiration Monitoring in Dynamic Environments

Yanni Yang, Jiannong Cao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Respiration monitoring (RM) is essential for diagnosing and tracking respiratory diseases. Recently, RFID technology has enabled RM in a lightweight and cost-effective way by only attaching the tiny and cheap RFID tag on the monitored person's chest. However, current systems are mostly designed for static environments with no surrounding people's movements. In reality, dynamic environments where people could move nearby the monitored person are quite common. In such environments, respiration signals would be disturbed by the dynamic multipath signals from ambient movements, which may lead to inaccurate RM results. In this paper, we study how to realize robust RFID-based RM in dynamic environments with accurate respiration rate estimation and apnea detection. We find that the dynamic multipath signals can cause not only high-frequency noises but also fake and distorted respiration cycles, which cannot be simply removed by the low-pass filter. Thus, we need a new method to eliminate the effect of multipath signals. Inspired by the intrinsic features of human respiration pattern, we propose to transform the respiration pattern into a matched filter, which can extract the real respiration cycles out of noisy RFID signals. We then estimate the respiration rate by counting the respiration cycles via multi-scale peak detection. For apnea detection, the problem from multipath signals is that the fake respiration cycles can result in the missing detection of apnea when the monitored person stops breathing. To address this issue, we define a new indicator which measures the dominance of respiration components in the signal's spectrum to identify apnea from multipath signals. Experimental results show that our system achieves an average error of 0.5 bpm for respiration rate estimation and a 5.3% error for apnea detection in dynamic environments.

Original languageEnglish
Title of host publication2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
PublisherIEEE Computer Society
Pages1-9
Number of pages9
ISBN (Electronic)9781728166308
DOIs
Publication statusPublished - Jun 2020
Event17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 - Virtual, Online, Italy
Duration: 22 Jun 202025 Jun 2020

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
Country/TerritoryItaly
CityVirtual, Online
Period22/06/2025/06/20

Keywords

  • Multipath effect
  • Respiration monitoring
  • RFID

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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