WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism

TL;DR

This paper presents a WiFi-based gesture recognition method that uses attention mechanisms and Doppler spectra to achieve high accuracy in both in-domain and cross-domain scenarios, outperforming existing solutions.

Contribution

It introduces a novel attention-based neural network leveraging Doppler spectra and ResNet18 for robust cross-domain gesture recognition using WiFi signals.

Findings

Achieves 99.72% in-domain accuracy.

Attains 97.61% cross-domain recognition accuracy.

Significantly outperforms existing methods.

Abstract

While fulfilling communication tasks, wireless signals can also be used to sense the environment. Among various types of sensing media, WiFi signals offer advantages such as widespread availability, low hardware cost, and strong robustness to environmental conditions like light, temperature, and humidity. By analyzing Wi-Fi signals in the environment, it is possible to capture dynamic changes of the human body and accomplish sensing applications such as gesture recognition. Although many existing gesture sensing solutions perform well in-domain but lack cross-domain capabilities (i.e., recognition performance in untrained environments). To address this, we extract Doppler spectra from the channel state information (CSI) received by all receivers and concatenate each Doppler spectrum along the same time axis to generate fused images with multi-angle information as input features. Furthermore, inspired by the convolutional block attention module (CBAM), we propose a gesture recognition network that integrates a multi-semantic spatial attention mechanism with a self-attention-based channel mechanism. This network constructs attention maps to quantify the spatiotemporal features of gestures in images, enabling the extraction of key domain-independent features. Additionally, ResNet18 is employed as the backbone network to further capture deep-level features. To validate the network performance, we evaluate the proposed network on the public Widar3 dataset, and the results show that it not only maintains high in-domain accuracy of 99.72%, but also achieves high performance in cross-domain recognition of 97.61%, significantly outperforming existing best solutions.