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

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.