Robust Cross-Scenario WiFi Wireless Sensing Using Incremental Learning and Elastic Weight Consolidation Loss

WiFi-based wireless sensing has emerged as a promising passive sensing technology that is precious for human activity recognition (HAR) across diverse applications. However, achieving robustness across varying scenarios presents a significant challenge, limiting its broader adoption. To address this issue, we propose a robust cross-scenario incremental learning (IL) method for WiFi-based wireless sensing that leverages WiFi channel state information (CSI) and elastic weight consolidation (EWC) loss. Our approach integrates a convolutional neural network and attention-based long short-term memory (CNN-ABLSTM) framework, which effectively captures the spatial and temporal features of CSI data. The IL strategy enhances model adaptability across dynamic environments, while EWC minimizes catastrophic forgetting by preserving critical weights from prior tasks. The method’s integration of a memory set and EWCLoss enables it to balance the retention of learned features with adaptation to new scenarios, effectively mitigating performance degradation across tasks. Experimental results on the MM-Fi dataset demonstrate robust cross-scenario performance: starting with initial training on scene E01, the model achieves incremental recognition in new scenes E02, E03, and E04 with cross-scenario accuracies of 88.01%, 80.16%, and 70.93%, respectively. The proposed approach substantially improves cross-scenario adaptability and test accuracy compared to traditional and cross-domain methods such as transfer learning. This work marks a significant advancement toward robust and scalable WiFi-based wireless sensing for diverse real-world applications.