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Simultaneous Estimation of Digit Tip Forces and Hand Postures in a Simulated Real-Life Condition With High-Density

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    This study demonstrates that deep learning with high-density surface electromyography (HD-sEMG) can continuously estimate hand forces and postures. This advance is crucial for developing more functional prosthetic systems.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Machine Learning

    Background:

    • Continuous estimation of hand forces and postures is vital for advanced myoelectric control.
    • Existing research often focuses on discrete hand movements, limiting practical prosthetic applications.
    • Daily activities require simultaneous estimation of grasp types and fingertip forces.

    Purpose of the Study:

    • To investigate the feasibility of continuously estimating multiple degrees of freedom in hand movements.
    • To develop and validate a framework for simultaneous estimation of fingertip forces and hand grasp types.
    • To apply deep learning techniques to high-density surface electromyography (HD-sEMG) data.

    Main Methods:

    • A reach and grasp framework was developed.
    • Deep learning models, specifically a 3-dimensional Convolutional Neural Network (3DCNN) combined with a Long Short-term Memory (LSTM) network, were utilized.
    • The models were trained and tested on HD-sEMG data from subjects performing four daily life grasp types while exerting varying forces.

    Main Results:

    • The combined 3DCNN-LSTM model reliably estimated absolute fingertip forces with a Mean Absolute Error (MAE) of 0.46 ± 0.23 and a Pearson Correlation Coefficient (PCC) of 0.90 ± 0.03%.
    • Hand posture classification achieved an MAE of 0.04 ± 0.01 and a PCC of 0.97 ± 0.02%.
    • Simultaneous estimation of grasp types and forces was successfully demonstrated.

    Conclusions:

    • Deep learning models applied to HD-sEMG data can effectively and continuously estimate both absolute fingertip forces and hand postures.
    • This approach shows significant promise for enhancing the functionality of prosthetic limbs.
    • The findings support the integration of advanced machine learning for sophisticated myoelectric control systems.