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Related Experiment Video

Updated: Jul 16, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

DUAL-Net: Joint Domain-Invariant and User-Adaptive Feature Learning for Gesture Recognition.

Shuangjiao Zhai¹, Bo Yang², Zixin Dai¹

¹School of Computer Science and Technology, North University of China, Taiyuan 030051, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

This study introduces DUAL-Net, a novel framework for WiFi-based gesture recognition. DUAL-Net enhances accuracy in cross-user scenarios by learning both domain-invariant and user-adaptive representations.

Keywords:

WiFi CSI WiFi–vision fusion cross-user adaptation diffusion generation domain-invariant learning gesturerecognition

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Area of Science:

Computer Science
Human-Computer Interaction
Ubiquitous Computing

Background:

WiFi-based gesture recognition offers contactless sensing but suffers from performance degradation due to environmental and user variations.
Existing methods often overlook crucial user-specific characteristics in favor of domain-invariant representations, limiting cross-user accuracy.

Purpose of the Study:

To develop a robust WiFi-based gesture recognition system that overcomes cross-user performance degradation.
To propose a novel dual-branch framework, DUAL-Net, that jointly models domain-invariant and user-adaptive features.

Main Methods:

DUAL-Net utilizes a contrastive fusion learning (CFL) module with modality-specific encoders for complementary WiFi and vision representations.
A spatial matrix difference (SMD)-guided cross-modal generation (CMG) module generates user-adaptive WiFi features using skeletal structural priors.
A two-stage learning framework enables offline adaptation to reduce online computational overhead.

Main Results:

DUAL-Net demonstrates superior cross-user gesture recognition performance compared to existing single-modality and multimodal methods.
SMD-guided conditioning improved recognition accuracy by up to 8.79% compared to diffusion generation without structural guidance.
Experiments were validated on the MM-Fi dataset and a self-collected dataset.

Conclusions:

DUAL-Net effectively addresses the challenge of user-specific differences in WiFi-based gesture recognition.
The proposed framework achieves state-of-the-art performance in cross-user scenarios, enhancing the practicality of ubiquitous computing applications.
Integrating structural priors via SMD-guided CMG significantly boosts recognition accuracy.