Transformer Network-based Gait Identification using WiFi

Wireless sensing has become a prominent choice for human activity recognition, valued for its non-intrusive operation and privacy-conscious design. However, it grapples with environmental challenges, especially from static (e.g., furniture) and dynamic objects (e.g., people walking), as well as how demographic factors such as BMI and sensor quantity affect accuracy. This paper addresses these gaps through a focused experiment on gait-based participant identification. Our methods and techniques encompass variance exploration for movement detection, Channel Power Distribution (CPD) analysis, and polynomial fitting to determine walking direction.We present a novel model for gait recognition, using a hybrid architecture that combines convolutional layers, LSTM blocks with residual connections, and multi-head self-attention mechanisms. We conducted a comprehensive evaluation of our gait identification system using two datasets: MultiEnvironment and HWDD. Both consist of data from 10 volunteers. We achieved 96.1% and 96.66% accuracy for MultiEnvironment and HWDD, respectively. To benchmark our system, we selected four state-of-the-art pre-trained models: Transformer, LSTM, CNN and SVM. Finally, we benchmark our technique against a dataset that we collected for 25 individuals, demonstrating an accuracy better than other state-of-the-art techniques of 97.9%.