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Enhancing the Myoelectric Pattern Recognition Robustness to Electrode Shift by an Autoencoder-Based Feature

Ge Gao, Yao Li, Yunfei Liu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 3, 2025
    PubMed
    Summary

    This study introduces an autoencoder-based method to improve myoelectric pattern recognition (MPR) robustness against electrode shift. The novel approach enhances classification accuracy without needing extra calibration data.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Machine Learning

    Background:

    • Electrode shift significantly impacts myoelectric pattern recognition (MPR) system robustness.
    • Existing solutions struggle to balance plug-and-play compatibility with high classification accuracy under electrode shift.

    Purpose of the Study:

    • To develop a novel adaptive calibration method for myoelectric pattern recognition (MPR) systems to overcome electrode shift challenges.
    • To enhance the robustness and accuracy of MPR systems without requiring additional calibration data.

    Main Methods:

    • A feature transformation approach (interpolation, translation, down-sampling) generated simulated shifted feature maps.
    • An autoencoder network learned resilient feature representations by minimizing reconstruction error.
    • The trained autoencoder served as an independent feature calibrator for classifiers.

    Main Results:

    • The proposed method achieved 87.20±3.53% classification accuracy under electrode shift conditions.
    • The autoencoder-based calibrator significantly outperformed three common comparison methods (p < 0.05).
    • The approach demonstrated effective mitigation of electrode shift interference.

    Conclusions:

    • The developed autoencoder-based feature calibrator offers a practical solution for enhancing MPR system robustness against electrode shift.
    • This method improves classification accuracy and plug-and-play compatibility without additional calibration, advancing MPR technology.