TRGR: Transmissive RIS-aided Gait Recognition Through Walls

TL;DR

This work tackles the challenge of identifying individuals via gait using RF signals when walls obstruct LOS paths. It introduces TRGR, a transmissive RIS-aided system that leverages mainly the magnitude of CSI, enhanced by a configuration alternating optimization to maximize the received SNR, formalized as $y=\\mathbf{h}\\mathbf{\\Phi}\\mathbf{H}x+w$ with SNR $\\rho=\\frac{|\\mathbf{h}\\mathbf{\\Phi}\\mathbf{H}|^2}{\\sigma^2}$, and validates it on a real prototype. A residual CNN (RCNN) backbone is designed to learn robust gait features from CSI magnitude, achieving 97.88% average accuracy through concrete walls. The results demonstrate the feasibility and robustness of transmissive RIS in wall-penetrating RF-based gait recognition, with strong potential for deployment in intelligent IoT and smart spaces.

Abstract

Gait recognition with radio frequency (RF) signals enables many potential applications requiring accurate identification. However, current systems require individuals to be within a line-of-sight (LOS) environment and struggle with low signal-to-noise ratio (SNR) when signals traverse concrete and thick walls. To address these challenges, we present TRGR, a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system. TRGR can recognize human identities through walls using only the magnitude measurements of channel state information (CSI) from a pair of transceivers. Specifically, by leveraging transmissive RIS alongside a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. Furthermore, a residual convolution network (RCNN) is proposed as the backbone network to learn robust human information. Experimental results confirm the efficacy of transmissive RIS, highlighting the significant potential of transmissive RIS in enhancing RF-based gait recognition systems. Extensive experiment results show that TRGR achieves an average accuracy of 97.88\% in identifying persons when signals traverse concrete walls, demonstrating the effectiveness and robustness of TRGR.

Figures (7)

• Figure 1: Logic circuit controlling board (left side), photography of the 1-bit transmissive RIS prototype (middle side), and the layout of the transmissive RIS element (right side).
• Figure 2: Architecture of the proposed transmissive RIS-aided through-wall gait recognition system.
• Figure 3: Components of the transceiver module.
• Figure 4: Comparison of raw and denoising data on the 5200th subcarrier in the CSI stream.
• Figure 5: CSI data of different subjects and vacant situation.