Real-Time Human Activity Recognition Using Wi-Fi CSI: A Deep CNN Architecture Approach

Human activity recognition (HAR) using Wi-Fi signals has gained significant attention due to its non-invasive nature, ubiquity, and respect for privacy, in contrast to camera-based systems. This study aims to develop a robust deep learning model for real-time HAR using Channel State Information (CSI) from Wi-Fi signals. We propose a deep Convolutional Neural Network (CNN) architecture incorporating an attention mechanism to process CSI data and classify various human activities. Our model was trained and tested using a comprehensive dataset of Wi-Fi CSI, employing advanced data augmentation techniques to enhance model generalization. The proposed model achieved an impressive accuracy of 99.69%, significantly outperforming existing approaches in terms of precision, recall, and F1 score. These findings demonstrate the potential of using Wi-Fi CSI with deep CNNs and attention mechanisms for accurate and real-time HAR, paving the way for applications in smart homes, healthcare monitoring, and other domains requiring efficient and privacy-preserving activity recognition. Future work will explore adapting this methodology to a variety of datasets and integrating an emotion recognition system to further expand its capabilities.