Passive Radar for Opportunistic Monitoring in e-Health Applications

Wenda Li, Bo Tan, Robert Piechocki

Research output: Contribution to journalArticle (Academic Journal)peer-review

34 Citations (Scopus)
314 Downloads (Pure)


This paper proposes a passive Doppler radar as a non-contact sensing method to capture human body movements, recognize respiration and physical activities in e-Health applications. The system uses existing in-home wireless signal as the source to interpret human activity. This work shows that passive radar is a novel solution for multiple healthcare applications which complements traditional smart home sensor systems. An innovative two-stage signal processing framework is outlined to enable the multi-purpose monitoring function. The first stage is to obtain premier Doppler information by using the high speed passive radar signal processing. The second stage is the functional signal processing including micro Doppler extraction for breathing detection and support vector machine (SVM) classifier for physical activity recognition. The experimental results show that the proposed system provides adequate performance for both purposes, and prove that non-contact passive Doppler radar is a complementary technology to meet the challenges of future healthcare applications.

Original languageEnglish
JournalIEEE Journal of Translational Engineering in Health and Medicine
Early online date25 Jan 2018
Publication statusE-pub ahead of print - 25 Jan 2018

Structured keywords

  • Digital Health


  • Activity Recognition
  • Breathing Detection
  • Doppler Radar
  • e-Health
  • Opportunistic Sensing
  • Passive Sensing


Dive into the research topics of 'Passive Radar for Opportunistic Monitoring in e-Health Applications'. Together they form a unique fingerprint.

Cite this