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

Renyuan Zhang; Siyang Cao

doi:10.1109/LSENS.2018.2889060

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

Renyuan Zhang
, Siyang Cao

Electrical and Computer Engr

Research output: Contribution to journal › Article › peer-review

169 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 language	English (US)
Article number	8585077
Journal	IEEE Sensors Letters
Volume	3
Issue number	2
DOIs	https://doi.org/10.1109/LSENS.2018.2889060
State	Published - Feb 2019

Keywords

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

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering

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10.1109/LSENS.2018.2889060

Cite this

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title = "Real-Time Human Motion Behavior Detection via CNN Using mmWave Radar",

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.",

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

author = "Renyuan Zhang and Siyang Cao",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2019",

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AB - 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.

KW - Microwave/millimeter wave sensors

KW - RF

KW - behavior detection

KW - convolution neural network (CNN)

KW - micro-Doppler effect

KW - micro-Doppler signature

KW - radar

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JF - IEEE Sensors Letters

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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