Domain Adaptation Across Configurations of FMCW Radar for Deep Learning Based Human Activity Classification

The application of convolutional neural networks (CNNs) for classification of human activities in frequency-modulated continuous-wave (FMCW) radar images has become widely adopted. However, complex measurement campaigns are needed for acquisition of large datasets - not only for varying activities but also for differing sensor settings such as e.g. deviating range and/or Doppler resolutions. In order to dramatically reduce cost and effort of those campaigns, this paper proposes deep domain adaptation techniques. These are based on four primary large datasets captured simultaneously with different sensor settings. The used datasets comprise five different human activities, each measured with the four varying settings, using Infineon's BGT60TR13C FMCW radar sensors operating at 60 GHz. This work emphasizes on the few-shot adversarial domain adaptation (FADA) algorithm, where we propose an improved version, showing better accuracy. The results are compared to the simple fine-tuning technique and the advanced Domain Adaptation using Stochastic Neighborhood Embedding (d-SNE) technique. We demonstrate the feasibility of the domain adaptation approach on radar data for human activity classification. Furthermore, we show that our improved FADA algorithm outperforms both conventional FADA and d-SNE while taking the same small amount of data in target domain captured with varying sensor settings.