WiFi-based human activity recognition (HAR) provides a non-intrusive approach for ubiquitous monitoring; however, achieving both high accuracy and robustness simultaneously remains a significant challenge. This paper proposes a Convolutional Neural Network with Enhanced Convolutional Block Attention Module (CNN-ECBAM) framework. The approach systematically converts raw Channel State Information (CSI) into pseudo-color images, effectively preserving essential signal characteristics for deep neural network processing. The core innovation is an Enhanced Convolutional Block Attention Module (ECBAM), tailored to CSI data characteristics, which integrates Efficient Channel Attention (ECA) and Multi-Scale Spatial Attention (MSSA). By employing learnable adaptive fusion weights, it achieves dynamic synergy between channel and spatial features, enabling the network to capture highly discriminative spatiotemporal patterns. The ECBAM module is integrated into a unified Convolutional Neural Network (CNN) to form the overall CNN-ECBAM model. Experimental results on the UT-HAR and NTU-FiHAR datasets demonstrate that CNN-ECBAM achieves competitive performance in recognition accuracy and outperforms mainstream baseline models. Specifically, it attains 99. 20% accuracy on UT-HAR (surpassing ResNet-18 at 98. 60%) and achieves 100% accuracy on NTU-FiHAR (exceeding GAF-CNN at 99. 62%). These results validate the effectiveness of the proposed method for high-precision and reliable WiFi-based HAR.
A Fri, study studied this question.