Motion Pattern Recognition via CNN-LSTM-Attention Model Using Array-Based Wi-Fi CSI Sensors in GNSS-Denied Areas
Year: 2025
Type: article
Abstract: Human activity recognition (HAR) is vital for applications in fields such as smart homes, health monitoring, and navigation, particularly in GNSS-denied environments where satellite signals are obstructed. Wi-Fi channel state information (CSI) has emerged as a key technology for HAR due to its wide coverage, low cost, and non-reliance on wearable devices. However, existing methods face challenges including significant data fluctuations, limited feature extraction capabilities, and difficulties in recognizing complex movements. This study presents a novel solution by integrating a multi-sensor array of Wi-Fi CSI with deep learning techniques to overcome these challenges. We propose a 2 × 2 array of Wi-Fi CSI sensors, which collects synchronized data from all channels within the CSI receivable range, improving data stability and providing reliable positioning in GNSS-denied environments. Using the CNN-LSTM-attention (C-L-A) framework, this method combines short- and long-term motion features, enhancing recognition accuracy. Experimental results show 98.2% accuracy, demonstrating superior recognition performance compared to single Wi-Fi receivers and traditional deep learning models. Our multi-sensor Wi-Fi CSI and deep learning approach significantly improve HAR accuracy, generalization, and adaptability, making it an ideal solution for GNSS-denied environments in applications such as autonomous navigation and smart cities.
Source: Electronics
Authors: Ming Xia, Shengmao Que, Nanzhu Liu, Qu Wang, Tuan Li
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409442177