Abstract
Recently, WiFi-based human activity recognition (HAR) has emerged as a promising technique for human-computer interactions, owing to its widespread availability and noninvasiveness. However, deploying WiFi-based HAR systems in new environments often results in performance degradation. Existing WiFi-based HAR systems across different environments typically assume identical category spaces between the source and target, an assumption challenged by practical scenarios. In this article, we present WiCAU, a comprehensive adaptation with uncertainty awareness for WiFi-based HAR across environments, designed to tackle the challenges of environments with unequal category spaces-a scenario known as partial domain adaptation (PDA). Different from conventional PDA methods that usually focus on training the feature extractor to align feature distributions or implement separate reweighting models to adjust source domain feature weights, WiCAU integrates feature alignment and source data reweighting to mitigate the risk of negative transfer. It also introduces an uncertain complement entropy to effectively handle uncertainty within the source environment. Moreover, WiCAU uses a hybrid network that combines wavelet analysis with deep neural networks to capture both the temporal and spatial dynamics present in WiFi channel state information (CSI) data. WiCAU's superior performance in PDA scenarios for HAR is demonstrated through comprehensive experiments with both self-built and publicly available datasets.