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A Conditional GAN-based Framework for Sparse sEMG Data Augmentation with Muscle Synergy Prior Constraints.

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    This study introduces a novel Muscle Synergy-Constrained Conditional GAN (MS-cGAN) to generate realistic multi-channel surface electromyography (sEMG) signals. The MS-cGAN framework overcomes limitations of existing methods, improving data authenticity and deep learning model performance.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Signal Processing

    Background:

    • High-quality surface electromyography (sEMG) data is scarce due to ethical and privacy concerns, hindering deep learning model development.
    • Existing generative models struggle with inter-channel correlations and physiological plausibility in multi-channel sEMG signals.
    • Error accumulation and lack of biological fidelity limit clinical applications of current sEMG generation techniques.

    Purpose of the Study:

    • To develop a novel framework for generating physiologically plausible multi-channel sEMG signals.
    • To address limitations in existing generative methods, including error accumulation and inadequate modeling of inter-channel relationships.
    • To enhance the biological fidelity and clinical utility of synthetic sEMG data.

    Main Methods:

    • Proposed a Muscle Synergy-Constrained Conditional Generative Adversarial Network (MS-cGAN) framework.
    • Introduced a Graph Convolutional Network (GCN)-based generator to model inter-channel relationships in sparse sEMG signals.
    • Integrated Muscle Synergy (MS) prior constraints as dynamic loss functions to ensure physiological plausibility.

    Main Results:

    • MS-cGAN successfully generates multi-channel sEMG signals with improved authenticity and bio-mechanical fidelity.
    • The GCN-based generator effectively captures complex inter-channel correlations, mitigating error accumulation.
    • Experiments demonstrated superior performance of MS-cGAN over traditional GANs and diffusion models on sEMG datasets.
    • Generated data significantly enhanced downstream task performance and kinematic prediction precision.

    Conclusions:

    • The MS-cGAN framework offers a robust solution for generating high-fidelity synthetic sEMG data.
    • This approach effectively supplements scarce sEMG datasets, enabling more reliable deep learning model training.
    • The method ensures physiological consistency, making generated sEMG signals suitable for clinical applications and improving predictive accuracy.