A new method for indoor non-rhythmic human motions classification using ultra-wideband radar

Chuanwei Ding, Heng Zhao, Zhicheng Liao, Li Zhang, Changzhi Li

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

Abstract

In order to classify non-rhythmic human motions by ultra-wideband (UWB) radar, a weighted rang-time-frequency transform (WRTFT) method is proposed to extract both Doppler and range features. And the bootstrap-aggregated decision trees (Bagged Trees) classifier is utilized to recognize each non-rhythmic human motion. Experiments show that the proposed method can achieve 92.9% classification accuracy for recognizing seven typical non-rhythmic activities.

Original languageEnglish
Title of host publication2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780996007856
StatePublished - Sep 26 2017
Event2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017 - Suzhou, China
Duration: Aug 1 2017Aug 4 2017

Publication series

Name2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017

Conference

Conference2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017
CountryChina
CitySuzhou
Period08/1/1708/4/17

Keywords

  • Bagged Trees
  • Ultra-wideband radar
  • non-rhythmic human motion
  • range-time-frequency analysis

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