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Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware

Yuzhou Lin, Ramaswamy Palaniappan, Philippe De Wilde

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 6, 2023
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    Summary
    This summary is machine-generated.

    This study introduces an uncertainty-aware model for robust hand grasp recognition using surface electromyography (sEMG). The new model significantly improves prosthetic control accuracy by intelligently rejecting uncertain movements.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Rehabilitation Technology

    Background:

    • Surface electromyography (sEMG) is a promising bio-signal for intuitive prosthetic limb control.
    • Long-term robustness of sEMG-based hand grasp recognition remains a significant challenge due to signal variability and class confusion.
    • Existing methods often struggle with reliable performance over extended periods and diverse conditions.

    Purpose of the Study:

    • To develop a novel uncertainty-aware model for robust, long-term hand grasp recognition using sEMG.
    • To address the limitations of current sEMG recognition systems by incorporating multidimensional uncertainty estimation.
    • To improve the reliability and accuracy of prosthetic hand control for daily living activities.

    Main Methods:

    • Proposed an end-to-end evidential convolutional neural network (ECNN) model capable of generating multidimensional uncertainties (vacuity, dissonance).
    • Utilized the challenging NinaPro Database 6 benchmark dataset for evaluating hand grasp recognition.
    • Examined misclassification detection on a validation set to determine optimal rejection thresholds, avoiding heuristic methods.
    • Conducted extensive comparisons of accuracy under non-rejection and rejection schemes for 8 hand grasps across 8 subjects.

    Main Results:

    • The ECNN achieved 51.44% accuracy without rejection and 83.51% accuracy with rejection, outperforming the state-of-the-art.
    • The rejection scheme significantly improved performance by 13.88% compared to non-rejection methods.
    • Recognition accuracy remained stable over 3 days, demonstrating robustness and minimal degradation.
    • The model demonstrated significant improvements over existing state-of-the-art methods by 3.71% (without rejection) and 13.88% (with rejection).

    Conclusions:

    • The proposed evidential convolutional neural network (ECNN) offers a robust solution for long-term sEMG-based hand grasp recognition.
    • Incorporating multidimensional uncertainties and a rejection strategy enhances recognition reliability for prosthetic control.
    • The developed model shows significant potential for creating accurate and stable prosthetic hand control systems.