WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism

• URL: http://arxiv.org/abs/2512.04521v1
• Date: Thu, 04 Dec 2025 07:09:13 GMT
• Title: WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism
• Authors: Ruijing Liu, Cunhua Pan, Jiaming Zeng, Hong Ren, Kezhi Wang, Lei Kong, Jiangzhou Wang,
• Abstract summary: We propose a gesture recognition network that integrates a multi-semantic attention mechanism with a self-attention-based channel mechanism.<n>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%.
• Score: 61.79272554643873
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Beyond Physical Labels: Redefining Domains for Robust WiFi-based Gesture Recognition [18.929824397974514]
  We propose GesFi, a novel WiFi-based gesture recognition system that introduces WiFi latent domain mining to redefine domains directly from the data itself.<n>Compared to state-of-the-art baselines, GesFi achieves up to 78% and 50% performance improvements over existing adversarial methods.
  arXiv  Detail & Related papers  (2026-01-07T11:40:02Z)
• Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing [20.1340684071988]
  We propose WiAnchor, a novel training framework for efficient cross-domain adaptation in the presence of incomplete activity categories.<n>We construct a comprehensive dataset to thoroughly evaluate WiAnchor, achieving over 90% cross-domain accuracy with absent activity categories.
  arXiv  Detail & Related papers  (2025-09-27T03:22:15Z)
• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• Wi-CBR: Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition [8.028748052177146]
  We propose a novel Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition (Wi-CBR)<n>Specifically, we propose a novel Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition (Wi-CBR)
  arXiv  Detail & Related papers  (2025-06-13T09:38:57Z)
• Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network [13.808977762165823]
  This paper introduces a novel deep learning model for cross-domain adaptation of Wi-Fi signals, inspired by physical signal shielding.<n>The system achieved 96% accuracy and demonstrated strong material discrimination capabilities, offering potential for use in security applications to identify concealed objects based on their composition.
  arXiv  Detail & Related papers  (2025-04-29T09:18:02Z)
• Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection [53.842568573251214]
  Experimental results on three SAR datasets demonstrate that our WBANet significantly outperforms contemporary state-of-the-art methods. Our WBANet achieves 98.33%, 96.65%, and 96.62% of percentage of correct classification (PCC) on the respective datasets.
  arXiv  Detail & Related papers  (2024-07-18T04:36:10Z)
• Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement [11.793803540713695]
  Inertial sensors are widely used in various portable devices. Wavelet dynamic selection network (WDSNet) intelligently selects appropriate wavelet basis for variable inertial signals. WDSNet, as a weakly-supervised method, achieves the state-of-the-art performance of all the compared fully-supervised methods.
  arXiv  Detail & Related papers  (2023-12-29T07:44:06Z)
• WiFi-based Spatiotemporal Human Action Perception [53.41825941088989]
  An end-to-end WiFi signal neural network (SNN) is proposed to enable WiFi-only sensing in both line-of-sight and non-line-of-sight scenarios. Especially, the 3D convolution module is able to explore thetemporal continuity of WiFi signals, and the feature self-attention module can explicitly maintain dominant features.
  arXiv  Detail & Related papers  (2022-06-20T16:03:45Z)
• A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition [53.41825941088989]
  A new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition. Our results show that WiVi data set satisfies the primary demand and all three branches in the proposed pipeline keep more than $80%$ of activity recognition accuracy.
  arXiv  Detail & Related papers  (2022-05-24T10:49:11Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.