Multi-AP CSI Fusion and Features Optimization-Based Behavioral Sensing on WiFi Platform

The prevalence of WiFi infrastructures has enabled the advancement of a wide range of WiFi-based intelligent applications. Behavioral sensing, as an increasingly popular example, is attracting growing research interest in developing a device-free, accurate, and privacy-friendly interaction. Recent advances in behavioral research have shown great promise, but most of existing systems fail to capture the full scope of the relationship between WiFi signals and behaviors. In this work, we propose a behavioral sensing framework by modeling channel state information (CSI) of WiFi signals. The key insight is to harness antenna diversity and model signals reflected off the human body from multiple WiFi access points (APs) to infer daily activities. To this end, a CSI tensor is first constructed by aggregating the CSI from all WiFi APs. Next, a CANDECAMP/PARAFAC (CP) approach is leveraged for factorizing this CSI tensor into several single-rank tensors as key features. Then, a features optimization strategy is applied to the extracted features for selecting those correlated and dominant features by an embedded graph learning and constraint. Finally, a recurrent neural network (RNN) model is used to take the optimized features and capture a robust mapping between the input and output for recognizing daily activities in homes without requiring a prior calibration and any explicit involvement from the users. We design and implement a behavioral sensing prototype reusing existing WiFi and then carry out comprehensive experiments in two indoor environments. Extensive experimental results suggest that our scheme can accurately and effectively recognize the users’ behaviors and also outperforms existing ones.