Style transfer for FMCW radar-based human activity recognition

Abstract

Human activity recognition is widely used in security monitoring, medical monitoring, human-computer interaction, and other fields. Radar-based activity identification plays an irreplaceable role in security and equipment independence, receiving extensive attention. This paper mainly studies human activity recognition based on Frequency Modulated Continuous Wave (FMCW). Learning the invariant stability characteristics under different scenes can improve the generalization ability of activity recognition. Inspired by the idea of image style transfer learning, the labeled training data can be transformed into styles of other scenes, to enhance the training data, improve data diversity and network generalization ability.