Cross-modal meta-learning for WiFi-based human activity recognition

WiFi-based Human Activity Recognition (HAR) faces challenges in achieving widespread deployment due to its reliance on massive data and limited scalability. However, the emergence of Few-Shot Learning (FSL) provides opportunities to address this issue. In this paper, we propose a cross-modal meta-learning approach based on Model-Agnostic Meta-Learning (MAML) to enable few-shot WiFi-based HAR. The hypothesis is that models can learn "learning methods" from thousands of diverse image classification tasks and apply them to WiFi-based HAR. By solely leveraging public image and WiFi signal datasets, the proposed approach trains a model capable of recognizing previously unseen activities with only 5 samples per class, achieving an average accuracy of 88.5% over thousands of tests.