Mean Teacher-Based Cross-Domain Activity Recognition Using WiFi Signals

WiFi channel state information (CSI)-based activity recognition has initiated a great many studies because of wide availability and privacy protection. However, general recognition approaches still struggle to generalize beyond the source domain of training data, i.e., well-trained models might not be suitable to target data with unseen subjects or environments. Existing solutions, such as few-shot learning-based and data augmentation-based approaches, either require a few labeled target samples which is difficult to be collected, especially, for old target users, or inappropriately treat augmented samples with different amounts of noise. To overcome these limitations, we propose a Mean Teacher-based cross-domain human activity recognition framework using WiFi CSI, WiTeacher. In this framework, to address the shift between source and target domains, we built a label smoothing-based classification loss, where the input data are the target-like samples generated by StyleGAN, and corresponding label values are dynamically adjusted by our designed adaptive label smoothing method. To enhance the model robustness, we devise a sample relation-based consistency regularization term to keep the distances of the two samples with and without perturbations invariant, which can exploit the relationships between samples to improve recognition performance. The experiments illustrate that WiTeacher achieves obvious gains without requiring any annotation data from the target domain.