Radar Human Motion Recognition Using Motion States and Two-Way Classifications

Moeness G. Amin, Ronny G. Guendel

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)


We perform classification of Activities of Daily Living (ADL) using a Frequency-Modulated Continuous Waveform (FMCW) radar. In particular, we consider contiguous motions that are inseparable in time. Both the micro-Doppler signature and range-map are used to determine transitions from translation (walking) to in-place motions and vice versa, as well as to provide motion onset and offset times. The in-place motion classes post and prior to walking can be separately handled by forward and backward in time classifiers. The paper describes ADL in terms of motion states and transitioning motions or activities, and sets forward a framework to deal with time separable and inseparable activities. It is shown that limiting the classes of activities, which are physically possible and associated with the current state, improves the classification rates compared to incorporating all ADL for any given time.
Original languageEnglish
Title of host publication2020 IEEE International Radar Conference, RADAR 2020
Number of pages6
ISBN (Electronic)9781728168128
Publication statusPublished - 2020
Externally publishedYes

Publication series

Name2020 IEEE International Radar Conference, RADAR 2020


  • Activities of daily living
  • Assisted living
  • Data fusion
  • Micro-Doppler
  • Range-map
  • Time-frequency representations


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