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Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model.

Lizhi Pan, Diyi Liu, Ruyi Wang

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an enhanced musculoskeletal model (MM) using a "divide and conquer" (DC) strategy and non-negative matrix factorization (NMF) for improved 3-DoF motion prediction from electromyography (EMG) signals.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Rehabilitation Engineering

    Background:

    • Musculoskeletal models (MMs) are crucial for decoding movement intent from electromyography (EMG) signals.
    • Decoding performance degrades with multi-degree-of-freedom (DoF) movements due to muscle signal crosstalk.
    • Existing models struggle with complex, coordinated limb movements.

    Purpose of the Study:

    • To develop an enhanced musculoskeletal model (MM) for accurate 3-DoF motion prediction.
    • To overcome the limitations of crosstalk in EMG signal decoding for coordinated movements.
    • To improve the precision of myoelectric control for hand prosthetics.

    Main Methods:

    • Proposed a novel enhanced musculoskeletal model (DC-NMF-MM) integrating a "divide and conquer" (DC) strategy with non-negative matrix factorization (NMF).
    • Collected surface EMG data from four muscles for wrist/MCP flexion/extension and eight channels for wrist pronation/supination from eight subjects.
    • Validated the DC-NMF-MM against two control models (T-NMF-MM, P-NMF-MM) and compared with artificial neural network and linear regression models using offline and online experiments.

    Main Results:

    • The proposed DC-NMF-MM demonstrated superior prediction performance compared to T-NMF-MM and P-NMF-MM in offline experiments.
    • DC-NMF-MM achieved higher accuracy in predicting joint angles than artificial neural network and linear regression models.
    • Online experiments confirmed the feasibility of the DC-NMF-MM for effective 3-DoF motion control, showing promising results in completion time, overshoots, and path efficiency.

    Conclusions:

    • The enhanced DC-NMF-MM effectively addresses EMG signal crosstalk for improved 3-DoF motion prediction.
    • This model offers a significant advancement for accurate and reliable myoelectric control systems.
    • The developed model provides a foundation for clinical applications in hand prosthetics and rehabilitation.