Continuous human activity recognition for arbitrary directions with distributed radars

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Abstract

Continuous Activities of Daily Living (ADL) recognition in an arbitrary movement direction using five distributed pulsed Ultra-Wideband (UWB) radars in a coordinated network is proposed. Classification approaches in unconstrained activity trajectories that render a more natural occurrence for Human Activity Recognition (HAR) are investigated. Feature and decision fusion methods are applied to the priorly extracted handcrafted features from the range-Doppler. A following multi-nomial logistic regression classifier, commonly known as Softmax, provides explicit probabilities associated with each target label. The outputs of these classifiers from different radar nodes were combined with a probability prediction balancing approach over time to improve performances. The final results show average improvements between 6.8% and 17.5% compared to the usage of any single radar in unconstrained directions
Original languageEnglish
Title of host publication2021 IEEE Radar Conference
Subtitle of host publicationRadar on the Move, RadarConf 2021
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-7609-3
ISBN (Print)978-1-7281-7610-9
DOIs
Publication statusPublished - 2021
Event2021 IEEE Radar Conference (RadarConf21): Radar on the Move - Atlanta, United States
Duration: 7 May 202114 May 2021

Publication series

NameIEEE National Radar Conference - Proceedings
Volume2021-May
ISSN (Print)1097-5659

Conference

Conference2021 IEEE Radar Conference (RadarConf21)
CountryUnited States
CityAtlanta
Period7/05/2114/05/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Micro-Doppler Classification
  • Distributed Radar
  • Assisted Living
  • Human Activity Recognition
  • Machine Learning

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