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    Summary
    This summary is machine-generated.

    This study introduces a Physics-Informed Bayesian Fusion (PI-BF) system to improve myoelectric control for lower limb prosthetics. PI-BF enhances gait phase recognition accuracy and stability, ensuring safer and more reliable assistive device performance.

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    Area of Science:

    • Biomedical Engineering
    • Robotics
    • Rehabilitation Technology

    Background:

    • Myoelectric pattern recognition systems are crucial for controlling lower limb prostheses and exoskeletons.
    • Signal variability in myoelectric control can lead to unsafe, unnatural gait transitions.

    Purpose of the Study:

    • To develop and validate a novel Physics-Informed Bayesian Fusion (PI-BF) post-processor.
    • To enhance the safety, reliability, and natural progression of gait in lower limb assistive devices.

    Main Methods:

    • Extracted Time-Domain (TD) and Time-Dependent Power Spectrum Descriptors (TD-PSD) features from surface electromyography (sEMG) signals.
    • Classified gait phases using SVM, ANN, KNN, and CNN-LSTM models.
    • Applied the PI-BF post-processor to classifier outputs, comparing it against Bayesian Fusion and no post-processing.

    Main Results:

    • PI-BF increased classification accuracy by up to 5.5%, reaching 85% on the SIAT-LLMD dataset with SVM.
    • Reduced Transition Detection Difference (TDD) to 0.1 ± 59.8 ms and improved output stability (INS index) by 5%.
    • Achieved consistent real-time gait phase recognition accuracies around 90%.

    Conclusions:

    • PI-BF effectively suppresses unstable transitions and promotes natural gait progression in myoelectric control.
    • The proposed PI-BF offers a practical, low-complexity solution for improving assistive lower-limb device performance.
    • PI-BF enhances the safety, reliability, and real-time capabilities of myoelectric control systems.