Health Secure Radar: Use of Micro Doppler Signatures for Health Care and Security Applications

  • Muhammad Muqtadir
  • , M. Haris Butt
  • , Daniyal Qazi
  • , Faran Awais Butt
  • , Ijaz Haider Naqvi
  • , Naveed Ul Hassan

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

7 Scopus citations

Abstract

Microwave-based radar sensors are increasingly been used for healthcare and security applications. The software defined implementation of the radars allows fall detection and classification of different types of motions enabling elderly care and monitoring without privacy invading cameras. In addition, such radar sensor allow seeing through visually opaque materials suitable for security applications. This paper investigates the use of micro-Doppler signatures of slowly moving objects to localize and detect and classify human micro-motions. Using the NI SDRs, we measure micro-Doppler signatures of various human motion scenarios. Thereafter, the micro Doppler signatures' data is augmented before being used to train a convolutional neural network that detects and identifies the fall events.

Original languageEnglish
Title of host publication17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665401203
DOIs
StatePublished - Aug 2021
Externally publishedYes
Event17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021 - Virtual, Osaka, Japan
Duration: 30 Aug 202131 Aug 2021

Publication series

Name17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021 - Proceedings

Conference

Conference17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021
Country/TerritoryJapan
CityVirtual, Osaka
Period30/08/2131/08/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Fall Detection
  • Micro Doppler signatures
  • Software defined radio
  • convolutional neural network (CNN)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing

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