  WiFi sensing technology has shown superiority in smart homes among various
sensors for its cost-effective and privacy-preserving merits. It is empowered
by Channel State Information (CSI) extracted from WiFi signals and advanced
machine learning models to analyze motion patterns in CSI. Many learning-based
models have been proposed for kinds of applications, but they severely suffer
from environmental dependency. Though domain adaptation methods have been
proposed to tackle this issue, it is not practical to collect high-quality,
well-segmented and balanced CSI samples in a new environment for adaptation
algorithms, but randomly-captured CSI samples can be easily collected.
{\color{black}In this paper, we firstly explore how to learn a robust model
from these low-quality CSI samples, and propose AutoFi, an annotation-efficient
WiFi sensing model based on a novel geometric self-supervised learning
algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that
are captured randomly, and then transfers the knowledge to specific tasks
defined by users, which is the first work to achieve cross-task transfer in
WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for
evaluation. The AutoFi transfers knowledge from randomly collected CSI samples
into human gait recognition and achieves state-of-the-art performance.
Furthermore, we simulate cross-task transfer using public datasets to further
demonstrate its capacity for cross-task learning. For the UT-HAR and Widar
datasets, the AutoFi achieves satisfactory results on activity recognition and
gesture recognition without any prior training. We believe that the AutoFi
takes a huge step toward automatic WiFi sensing without any developer
engagement.
