Functional Data Analysis Assisted Cross-Domain Wi-Fi Sensing Using Few-Shot Learning

Recent years have witnessed rapid development of Wi-Fi sensing applications. However, the domain shift problem is still an open problem. Variations in environment, time, and detected objects can undermine the effectiveness of cross-domain sensing. This paper proposes a few-shot learning framework for Wi-Fi sensing that enables generalization to unseen domains given only a few samples. To better extract stable features, functional data analysis (FDA) is first employed as a preprocessing technique. We thoroughly evaluate our approach to different Wi-Fi sensing tasks: gesture recognition, and activity recognition. Our experimental results demonstrate that FDA assisted system improves cross-domain accuracy by 14%, 10%, and 8% on the respective tasks with five samples per class.