Evaluation Metrics for Continuous Human Activity Classification Using Distributed Radar Networks

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

Abstract

Continuous Human Activity Recognition (HAR) in arbitrary directions is investigated using 5 spatially distributed pulsed Ultra-Wideband (UWB) radars. Such activities performed in arbitrary and unconstrained trajectories render a more natural occurrence of Activities of Daily Living (ADL) to be recognized. An innovative signal level fusion method was applied on the Range-Time (RT) maps, and deep learning classification via Recurrent Neural Networks (RNN) with and without bidi-rectionality was used on the computed micro-Doppler (μD) spectrogram. To assess classification performances, novel evaluation metrics accounting for the continuous nature of the sequence of activities and for imbalances in the dataset are proposed and compared with existing metrics. It is shown that conventional accuracy evaluation is too coarse, and that the proposed metrics need to be considered for a more comprehensive evaluation.
Original languageEnglish
Title of host publication2022 IEEE Radar Conference (RadarConf22) Proceedings
Place of PublicationPiscataway
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-5368-1
ISBN (Print)978-1-7281-5369-8
DOIs
Publication statusPublished - 2022
Event2022 IEEE Radar Conference
- New York City, United States
Duration: 21 Mar 202225 Mar 2022

Conference

Conference2022 IEEE Radar Conference
Abbreviated titleRadarConf22
Country/TerritoryUnited States
CityNew York City
Period21/03/2225/03/22

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
  • LSTM
  • Human Activity Recognition

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