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Related Experiment Video

Updated: Jul 2, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

Xiangdian Chen, Yan Liu, Heng Jin

    IEEE Journal of Biomedical and Health Informatics
    |June 30, 2026
    PubMed
    Summary

    This study introduces AdaWGAN, a new method to generate realistic surface electromyography (sEMG) data for better human intention recognition. This approach enhances gesture classification accuracy in human-computer interaction (HCI).

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    Area of Science:

    • Biomedical Engineering
    • Human-Computer Interaction (HCI)
    • Machine Learning

    Background:

    • High-quality labeled surface electromyography (sEMG) data is scarce, hindering human intention recognition and practical HCI applications.
    • Existing sEMG augmentation methods inadequately exploit spatial information from high-density sEMG (HD-sEMG) recordings.

    Purpose of the Study:

    • To propose a novel dual-branch Adaptive Weight Wasserstein Generative Adversarial Network (AdaWGAN) for physiologically consistent HD-sEMG data augmentation.
    • To improve the fidelity and category-alignment of generated HD-sEMG samples for enhanced gesture recognition.

    Main Methods:

    • AdaWGAN jointly learns spatiotemporal activation maps and frequency-band features from single-trial HD-sEMG data.
    • An adaptive weighting mechanism balances adversarial and classification losses to improve semantic consistency.

    Related Experiment Videos

    Last Updated: Jul 2, 2026

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

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

    Published on: March 28, 2025

  • The model generates high-fidelity, category-aligned HD-sEMG samples for gesture recognition tasks.

    • Main Results:

    • AdaWGAN synthesized samples demonstrated high fidelity, with Pearson correlation coefficients exceeding 0.95 for most gesture classes.
    • Achieved superior gesture classification accuracies (88.2±6.3% and 94.3±4.4%) on benchmark HD-sEMG datasets, outperforming state-of-the-art methods.
    • Attribution visualization confirmed that AdaWGAN captures physiologically meaningful muscle activation patterns.

    Conclusions:

    • AdaWGAN offers an effective solution for augmenting HD-sEMG data, addressing the data scarcity challenge in HCI.
    • The developed model contributes to interpretable and physiologically plausible generative models for biomedical HCI applications.