WiRD-Gest: Gesture Recognition In The Real World Using Range-Doppler Wi-Fi Sensing on COTS Hardware

TL;DR

WiRD-Gest introduces a Wi-Fi gesture recognition system using monostatic sensing on COTS hardware, achieving high accuracy and robustness in complex environments, and provides an open benchmark dataset.

Contribution

The paper presents the first monostatic Wi-Fi gesture recognition system with a comprehensive benchmark and dataset, improving robustness and generalization over prior methods.

Findings

High accuracy in crowded, dynamic environments

Robust performance with minimal degradation in real-world scenarios

Open source benchmark and dataset for future research

Abstract

Wi-Fi sensing has emerged as a promising technique for gesture recognition, yet its practical deployment is hindered by environmental sensitivity and device placement challenges. To overcome these limitations we propose Wi-Fi Range and Doppler (WiRD)-Gest, a novel system that performs gesture recognition using a single, unmodified Wi-Fi transceiver on a commercial off-the-shelf (COTS) laptop. The system leverages an monostatic full duplex sensing pipeline capable of extracting Range-Doppler (RD) information. Utilizing this, we present the first benchmark of deep learning models for gesture recognition based on monostatic sensing. The key innovation lies in how monostatic sensing and spatial (range) information fundamentally transforms accuracy, robustness and generalization compared to prior approaches. We demonstrate excellent performance in crowded, unseen public spaces with dynamic interference and additional moving targets even when trained on data from controlled environments only. These are scenarios where prior Wi-Fi sensing approaches often fail, however, our system suffers minor degradation. The WiRD-Gest benchmark and dataset will also be released as open source.