Human activity classification with radar signal processing and machine learning

Mu Jia, Shaoxuan Li , Julien Le Kernec, Shufan Yang, Francesco Fioranelli, Olivier Romain

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

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As the number of older adults increases worldwide, new paradigms for indoor activity monitoring are required to keep people living at home independently longer. Radar-based human activity recognition has been identified as a sensing modality of choice because it is privacy-preserving and does not require end-users compliance or manipulation. In this paper, we explore the robustness of machine learning algorithms for human activity recognition using six different activities from the University of Glasgow dataset recorded with an FMCW radar. The raw radar data is pre-processed and represented using four different domains, namely, range-time, range-Doppler amplitude and phase diagrams, and Cadence Velocity Diagram. From those, salient features can be extracted and classified using Support Vector Machine, Stacked AutoEncoder, and Convolutional Neural Networks. The fusion of handcrafted features and features from CNN is applied to get the best scheme of classification with over 96% accuracy.
Original languageEnglish
Title of host publication2020 International Conference on UK-China Emerging Technologies (UCET)
Number of pages5
ISBN (Electronic)978-1-7281-9488-2
ISBN (Print)978-1-7281-9489-9
Publication statusPublished - 2020
EventUCET 2020 : International Conference on UK-China Emerging Technologies (UCET) - Glasgow, United Kingdom
Duration: 20 Aug 202021 Aug 2020


ConferenceUCET 2020
CountryUnited Kingdom


  • Radar
  • signal processing
  • Machine Learning
  • deep learning
  • classification
  • healthcare
  • assisted living

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