A multi-stage few-shot framework for extensible radar-based human activity recognition

Abstract

This paper proposes a novel framework for radar-based indoor human activity recognition (HAR) using a multi-stage few-shot learning (FSL) paradigm. The core of our approach lies in the design of a dynamic feature extraction architecture that exploits wavelet convolution along with depthwise separable convolutions to effectively capture multi-scale and multi-frequency information from radar signals. We also propose a meta-learning-inspired mechanism that dynamically adjusts class weights for unseen categories, thereby enhancing adaptability and recognition accuracy in few-shot scenarios. Extensive experiments on five benchmark datasets demonstrate consistent performance gains over state-of-the-art methods, with substantial improvements observed for both seen and unseen classes. These findings highlight the robustness, scalability, and generalization capability of our framework, underscoring its potential to advance radar-based HAR in complex and diverse environments.