Work

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

Year: 2025

Type: article

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.

Source: WiPiEC Journal - Works in Progress in Embedded Computing Journal

DOI: 10.64552/wipiec.v11i1.92

Authors: Claire Béranger, Alexandre Bordat, Petr Dobias, Ngoc-Son Vu, Julien Le Kernec, David Guyard, Olivier Romain

Institutions: Centre National de la Recherche Scientifique, CY Cergy Paris Université, Equipes Traitement de l'Information et Systèmes, École Nationale Supérieure de l'Électronique et de ses Applications, University of Glasgow

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4413927245

FWCI:

Cites: 12

Cited by:

Related to: 10