Device-free cross location activity recognition via semi-supervised deep learning

Human activity recognition plays an important role in a variety of daily applications. There has been tremendous work on human activity recognition based on WiFi channel state information (CSI). Although achieving reasonable performance in certain cases, they are yet faced with a major challenge: location dependence. An activity recognition model trained at one location does not perform properly at other locations, because the human location also has significant influence on WiFi signal propagation. In this paper, we aim to solve the location dependence problem of CSI-based human activity recognition and propose a device-free cross location activity recognition (CLAR) method via semi-supervised deep learning. We regard the locations with labeled activity samples as the source domains and the locations with unlabeled activity samples as the target domains. By exploiting pseudo labeling and feature mapping, CLAR trains an activity recognition model working across the source and the target domains as well as the unseen domains which have no training samples. CLAR first extracts the trend component from the activity samples by Singular Spectrum Analysis (SSA), then annotates the unlabeled samples with the pseudo labels through a dual-score multi-classifier labeling model. The activity recognition model is trained using the labeled samples from the source domains and the pseudo-labeled samples from the target domains. Both the labeling and the recognition models are based on Bidirectional Long Short Term Memory (BLSTM). Evaluations in real-world environments demonstrate the effectiveness and generalization of the method CLAR, which performs well for both the source and the target domains, and generalizes well to the unseen domains.