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Automatic misclassification rejection for LDA classifier using ROC curves.

Radhika Menon, Gaetano Di Caterina, Heba Lakany

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary
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    This study introduces a method to reduce misclassifications in electromyography (EMG) pattern recognition for prosthetic control. By using ROC curves to set rejection thresholds, it improves the reliability of upper-limb prosthetics.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Rehabilitation Technology

    Background:

    • Electromyography (EMG) signals are crucial for controlling upper-limb prosthetic devices.
    • Distinguishing between subtle EMG patterns for multiple finger gestures remains challenging.
    • Misclassifications in EMG-based prosthetics lead to detrimental and unwanted limb movements.

    Purpose of the Study:

    • To develop a technique for rejecting misclassified predictions from LDA classifiers.
    • To enhance the reliability and controllability of EMG-based upper-limb prosthetics.
    • To minimize false positives in prosthetic control systems.

    Main Methods:

    • Implemented a method to automatically compute class-specific thresholds using Receiver Operating Characteristic (ROC) curves.
    • Discarded classification predictions with posterior probabilities below the computed threshold.
    • Tested the technique on surface EMG data from amputees performing seven hand gestures.

    Main Results:

    • Effectively reduced the number of misclassifications in EMG-based gesture recognition.
    • Demonstrated significant improvement in accuracy, especially in cases with initially low classification performance.
    • Validated the technique's efficacy in minimizing false positives for prosthetic control.

    Conclusions:

    • The proposed thresholding technique enhances the safety and usability of EMG-controlled upper-limb prosthetics.
    • Rejecting uncertain predictions significantly improves prosthetic device controllability.
    • This approach offers a practical solution for reducing erroneous movements in prosthetic applications.