Low-Data Cross-Environment Transfer Learning for Wi-Fi CSI-Based Human Activity Recognition: An Inductive Bias Perspective
TL;DR
A cross-environment transfer learning framework for CSI-based HAR that integrates CSI preprocessing, adaptive amplitude-phase fusion via TinyGate, an R(2+1)D backbone, and two temporal modeling strategies, namely Bidirectional Long Short-Term Memory (Bi-LSTM) and Transformer is proposed.
Abstract
Wi-Fi channel state information (CSI)-based human activity recognition (HAR) has emerged as a promising device-free and privacy-preserving sensing approach. However, its practical deployment remains challenging because models trained in one environment often suffer substantial performance degradation when transferred to a different environment, particularly when only limited labeled target data are available. To address this issue, this study proposes a cross-environment transfer learning framework for CSI-based HAR that integrates CSI preprocessing, adaptive amplitude-phase fusion via TinyGate, an R(2+1)D backbone, and two temporal modeling strategies, namely Bidirectional Long Short-Term Memory (Bi-LSTM) and Transformer. The framework is evaluated on the MultiEnv dataset under both single-source and multi-source transfer settings using four target supervision ratios: 5%, 10%, 20%, and 40%. Additional validation is conducted using the Widar 3.0 dataset, and an additional Transformer configuration analysis is performed to examine the effect of depth, warm-up, and regularization. Experimental results show that Bi-LSTM generally achieves higher target accuracy, smaller source-target accuracy gaps, and more consistent adaptation behavior than the Transformer under the evaluated low-data transfer settings. In contrast, the Transformer requires more careful architectural and training design, including deeper architectures, stronger regularization, and larger target-label budgets, before its temporal modeling capacity can be translated into competitive transfer performance. These findings are consistent with the interpretation that the sequential inductive bias of Bi-LSTM is better aligned with the temporal continuity, structured noise, and environment-dependent variation of CSI signals. Overall, this research provides empirical evidence and practical guidance for designing Wi-Fi CSI-based HAR systems under limited-data cross-environment transfer scenarios.