Learning Shared Semantic Representations from WiFi CSI for Unified Multi-Task Human Activity Recognition
Human activity recognition (HAR) plays a critical role in intelligent wireless sensing and mobile edge computing. Compared with traditional vision-based and wearable-based approaches, WiFi channel state information (CSI) enables privacy-preserving and device-free activity perception. CSI encodes human-induced channel variations that serve as a natural basis for activity-oriented semantic analytics over wireless networks. However, existing WiFi CSI-based methods suffer from weak cross-scene generalization, high model complexity, and poor multi-task collaboration. To address these issues, this paper proposes UniSense-CSI, a unified multi-task framework that jointly learns dynamic gesture recognition, static posture classification, and fall detection. Specifically, the proposed framework converts CSI signals into pseudo-RGB images, extracts spatio-temporal features using a customized ConvNeXt backbone, and leverages an improved PerceiverIO module to compress high-dimensional features into a compact latent space. Based on the shared representation, task queries and adapters are introduced to enable parallel multi-task inference within a common architecture. Experiments on public datasets demonstrate that the proposed framework achieves accuracies of 99.64%, 99.66% and 96.09% on the three tasks, respectively, while maintaining low inference latency and favorable edge-deployment capability.