Real-Time Human Motion Behavior Detection via CNN Using mmWave Radar

Renyuan Zhang, Siyang Cao

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

A real-time behavior detection system using millimeter wave radar is presented in this article. Radar is used to sense the micro-Doppler information of targets. A convolution neural network (CNN) is further implemented in the detection and classification of the human motion behaviors using this information. Both the convolution layers and architecture of CNNs are presented. The analysis on loss and accuracy of training results is also shown. The experimental result indicates a precise determination of human motion behavior detection using the proposed system.

Original languageEnglish (US)
Article number8585077
JournalIEEE Sensors Letters
Volume3
Issue number2
DOIs
StatePublished - Feb 2019
Externally publishedYes

Keywords

  • Microwave/millimeter wave sensors
  • RF
  • behavior detection
  • convolution neural network (CNN)
  • micro-Doppler effect
  • micro-Doppler signature
  • radar

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation

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