Contactless Respiration Monitoring using Wi-Fi and Artificial Neural Network Detection Method

Research output: Contribution to journalArticlepeer-review

93 Downloads (Pure)

Abstract

Detecting respiration in a non-intrusive manner is beneficial not only for convenience but also for cases where the traditional ways cannot be applied. This paper presents a novel simple low-cost system where ambient Wi-Fi signals are acquired by a third-party tool (Nexmon) installed in a Raspberry Pi and is able to detect the respiration time domain waveform of a person. This tool was selected as it uses 80 MHz bandwidth of the Wi-Fi signal and supports the latest implementations that are widely used, such as 802.11ac. A neural network is developed to detect the respiration frequency of the waveform. Generated waves emulating respiration waveforms were used for training, validating, and testing the model. The model can be applied to unseen real measurement data and successfully determine the breathing frequency with a very low average error of 4.7% tested in 20 measurement datasets.

Original languageEnglish
Pages (from-to)1297-1308
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume28
Issue number3
Early online date28 Nov 2023
DOIs
Publication statusPublished - Mar 2024

Keywords

  • artificial neural network (ANN)
  • Biomedical monitoring
  • Channel State Information (CSI)
  • Heart rate
  • IEEE 802.11n Standard
  • Monitoring
  • Receiving antennas
  • respiration frequency
  • Wi-Fi
  • Wireless communication
  • Wireless fidelity

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management

Fingerprint

Dive into the research topics of 'Contactless Respiration Monitoring using Wi-Fi and Artificial Neural Network Detection Method'. Together they form a unique fingerprint.

Cite this