Continuous AoA-ToF Maps Feature for Single-Pair WiFi sensing of Human Activity Recognition

Human activity recognition (HAR) via WiFi sensing has emerged as a pivotal technology for smart environments, offering device-free, privacy-preserving monitoring. However, existing methods often face limitations in feature representation efficiency and generalization under constrained hardware setups. In this work, we propose Continuous Angle-of-Arrival and Time-of-Flight Maps (CATM), a novel and easily learnable feature extraction framework that jointly encodes spatial and temporal dynamics of human activities using commercial WiFi devices. By integrating smoothed channel state information (CSI) with MUSIC-based signal processing, CATM constructs 2D heatmaps that unify angle-of-arrival (AoA) and time-of-flight (ToF) features, enabling robust representation of both coarse and fine-grained movements. Unlike conventional approaches relying on fragmented temporal or spectral features, CATM inherently embeds continuous spatiotemporal patterns that simplify downstream model learning. Our lightweight Res-BiLSTM network, trained on CATM features, achieves 93.2% accuracy across eight activities and five users, under three displacement ways and outperforming other single-domain methods. Crucially, CATM demonstrates exceptional transferability: when adapted to unseen device placements, the framework retains 80% accuracy with only 20% retraining data, significantly reducing dependency on extensive labeled datasets. These results demonstrate that CATM provides a more informative feature extraction method compared to traditional approaches relying on CSI amplitude and Doppler information for human activity recognition.