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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
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 25/12/2023

Publication status

Published - 25/12/2023

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

Publication series

  • Publication series name: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings

ISBN (Print)

9798350381719

ISBN (Electronic)

9798350381719

External Publication IDs

  • ORCID: /0000-0001-7666-838X/work/159526319
  • Scopus: 85182726615

Host publication title

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

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