Neurosymbolic AI Empowered Consumer Electronics Healthcare for WiFi-Based Human Activity Recognition

Human activity recognition (HAR) using WiFi enables non-intrusive monitoring in consumer electronics healthcare. However, it suffers from multipath fading, device heterogeneity and scarcely labeled fine-grained data. To address these challenges, we develop a neurosymbolic artificial intelligence (AI) architecture for WiFi-based HAR. The model first segments raw WiFi channel state information (CSI) into fixed-length windows. Each window is processed by three parallel encoders, i.e., a one-dimensional convolutional neural network for local feature extraction, a Transformer with positional encoding for capturing global context, and a multilayer perceptron (MLP) that generates rule-based embeddings. The outputs of these streams are then fused via a learnable multi-head cross-attention mechanism, which amplifies salient motion cues and suppresses noise. Finally, the fused sequence is averaged over time and classified under a combined data-driven and semantic-rule loss. Four benchmark datasets, i.e., SignFi, Widar3.0, UT-HAR, and NTU-HAR are used to train and test our model. Experimental results determined that our model achieves up to 99.72% accuracy, consistently outperforming state-of-the-art (SOTA) methods. This lightweight and transparent framework supports practical deployment of explainable HAR on resource-constrained consumer electronics.