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A Lightweight Hybrid Encoder-Decoder Framework for Multiple Degree of Freedom Muscle Force Estimation.

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    A novel hybrid framework improves multi-finger force estimation using electromyogram (EMG) decomposition and Temporal Firing Rate-Net. This method enhances prediction accuracy and computational efficiency for robust human-machine interfaces.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Rehabilitation Technology

    Background:

    • Electromyogram (EMG) decomposition offers superior motor unit (MU) discharge information for finger motion decoding.
    • Existing multi-degree-of-freedom (DoF) force estimation methods are limited by neglecting temporal MU discharge effects and rigid MU pool allocation.

    Purpose of the Study:

    • To develop and evaluate a hybrid encoder-decoder framework for improved multi-DoF muscle force estimation.
    • To address limitations in temporal force accumulation and MU pool allocation in EMG-based force prediction.

    Main Methods:

    • A hybrid framework integrating EMG decomposition with Temporal Firing Rate-Net (TFR-Net) was proposed.
    • The framework utilizes temporal firing rate to model force accumulation and dynamically allocates MU weights.
    • EMG signals were decomposed into MU spike trains and firing rates for hierarchical encoding and decoding.

    Main Results:

    • The proposed method achieved superior performance compared to baseline methods, with higher correlation (R²: 0.80 ± 0.12) and lower prediction error (RMSE: 6.32% ± 1.89% MVC).
    • Demonstrated improved computational efficiency compared to the twitch force model method.
    • Evaluated on 15-min EMG data from 10 subjects performing multi-finger isometric extensions.

    Conclusions:

    • The developed hybrid framework shows significant potential for accurate and efficient dexterous finger force prediction.
    • Further advancements could lead to robust human-machine interfaces for realistic applications.
    • The method overcomes key constraints in current EMG-based force estimation techniques.