WiFi-based human activity recognition (HAR) is a promising solution for enabling intelligent sensing in autonomic systems such as smart homes and industrial environments. Recent research has explored deep learning models for this task, yet two core challenges persist, i.e., signal degradation caused by environmental noise, and the difficulty of extracting discriminative features from heterogeneous time-frequency domains. To address these limitations, we propose a generative artificial intelligence (GenAI) framework that integrates a conditional diffusion model with a pixel-aware attention mechanism. The diffusion model enhances data quality by reconstructing clean channel state information (CSI) signals through a forward noise injection and a reverse denoising process. The pixel-aware attention module adaptively fuses multi-resolution features from short-time Fourier transform (STFT) and discrete wavelet transform (DWT) spectrograms at channel, spatial, and pixel levels, improving the representation of fine-grained activity patterns. To the best of our knowledge, this is the first work to apply a generative denoising diffusion with fine-grained pixel-level fusion for this task. We evaluate our model on four public datasets, i.e., SignFi, Widar3.0, UT-HAR, and NTU-HAR. Experimental results show that our model consistently outperforms existing methods, demonstrating strong robustness and generalization in both gesture and action recognition tasks.
Xu et al. (Thu,) studied this question.
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