Contactless Breathing Waveform Detection Through RF Sensing: Radar vs. Wi-Fi Techniques

Umer Saeed, Dingchang Zheng, Behzad Ali Shah, Syed Ikram Shah, Sana Ullah Jan, Jawad Ahmad, Qammer Hussain Abbasi, Syed Aziz Shah, Wadii Boulila

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

Human breathing detection plays a vital role in healthcare, safety, and various other applications. This research paper explores the use of radio-frequency (RF) sensing technologies, specifically radar and Wi-Fi, for detecting human breathing patterns. Abnormal breathing patterns can indicate respiratory or cardiovascular diseases, and early detection is crucial for timely diagnosis and treatment. Radar-based systems utilize low-power RF pulses to capture subtle chest movements associated with breathing, while software-defined radio (SDR)-based systems analyze Wi-Fi signals to detect variations caused by human chest motion. Deep learning algorithms, namely residual neural network (ResNet) and deep multilayer perceptron (DMLP), are employed to classify breathing patterns based on the collected data. ResNet attained classification accuracy up to 90% on radar-based spectrogram images data, while DMLP attained classification accuracy up to 99% on SDR-based channel state information data. The proposed approaches offer non-intrusive, remote-operable, and cost-effective solutions for breathing detection. The research demonstrates the potential of RF sensing technologies in healthcare, eldercare, sleep monitoring, and emergency response systems, paving the way for enhanced well-being and safety.
Original languageEnglish
Title of host publication2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings
PublisherIEEE
Pages1-10
Number of pages10
ISBN (Electronic)9798350381719
ISBN (Print)9798350381726
DOIs
Publication statusPublished - 25 Dec 2023
Event10th International Conference on Communications and Networking - Hammamet, Tunisia
Duration: 1 Nov 20233 Nov 2023
https://comnet.ieee.tn/

Publication series

Name2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings

Conference

Conference10th International Conference on Communications and Networking
Abbreviated titleComNet’2023
Country/TerritoryTunisia
City Hammamet
Period1/11/233/11/23
Internet address

Keywords

  • artificial intelligence
  • deep learning
  • radio-frequency sensing
  • software-defined radio
  • wireless healthcare
  • breathing detection
  • radar
  • Wi-Fi

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