Autosen: improving automatic wifi human sensing through cross-modal autoencoder
This addresses the challenge of improving WiFi sensing for human activity recognition in uncontrolled environments, though it appears incremental as it builds on existing cross-modal methods.
The paper tackled the problem of WiFi human sensing being limited to controlled settings by introducing AutoSen, an automatic solution using a cross-modal autoencoder to extract features from unlabeled CSI data, achieving exceptional performance on a benchmark dataset.
WiFi human sensing is highly regarded for its low-cost and privacy advantages in recognizing human activities. However, its effectiveness is largely confined to controlled, single-user, line-of-sight settings, limited by data collection complexities and the scarcity of labeled datasets. Traditional cross-modal methods, aimed at mitigating these limitations by enabling self-supervised learning without labeled data, struggle to extract meaningful features from amplitude-phase combinations. In response, we introduce AutoSen, an innovative automatic WiFi sensing solution that departs from conventional approaches. AutoSen establishes a direct link between amplitude and phase through automated cross-modal autoencoder learning. This autoencoder efficiently extracts valuable features from unlabeled CSI data, encompassing amplitude and phase information while eliminating their respective unique noises. These features are then leveraged for specific tasks using few-shot learning techniques. AutoSen's performance is rigorously evaluated on a publicly accessible benchmark dataset, demonstrating its exceptional capabilities in automatic WiFi sensing through the extraction of comprehensive cross-modal features.