Human Activity Recognition Using SVM-based on micro-Doppler Radar Data Classification

Authors

  • Claire Béranger ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, F-95000 Cergy
  • Alexandre Bordat ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, F-95000 Cergy
  • Petr Dobias ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, F-95000 Cergy
  • Ngoc-Son Vu ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, F-95000 Cergy
  • Julien Le Kernec James Watt School of Engineering, University of Glasgow; ETIS Laboratory
  • David Guyard BlueLinea
  • Olivier Romain ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, F-95000 Cergy; University of Glasgow, UK

DOI:

https://doi.org/10.64552/wipiec.v11i1.92

Keywords:

Radar Spectrogram, Human Activity Recognition, Classification, SVM, Clinical Context

Abstract

In the context of an ageing population, research is increasingly focusing on the use of radar for fall detection to support clinical and teleassistance services. After the radar data are processed, a spectrogram of the recorded activity can be generated. We propose to extract characteristic parameters (such as Area, Perimeter or Orientation) from the activity signature contained in the spectrogram, using image processing techniques. Then, these data are input into a Support Vector Machine (SVM), which is more lightweight than other learning models. This method achieves an accuracy of 88.85%, providing an optimal solution with low resource requirements and similar or even improved performance relative to the state of the art.

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Published

2025-09-02

How to Cite

Béranger, C., Bordat, A., Dobias, P., Vu, N.-S., Le Kernec, J., Guyard, D., & Romain, O. (2025). Human Activity Recognition Using SVM-based on micro-Doppler Radar Data Classification. WiPiEC Journal - Works in Progress in Embedded Computing Journal, 11(1), 4. https://doi.org/10.64552/wipiec.v11i1.92

Issue

Vol. 11 No. 1 (2025): WiPiEC Journal — Special Issue

Section

Short Paper

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Copyright (c) 2025 Claire Béranger, Alexandre Bordat, Petr Dobias, Ngoc-Son Vu, Julien Le Kernec, David Guyard, Olivier Romain

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