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Asymmetric Effects of Different Training-Testing Mismatch Types on Myoelectric Regression via Deep Learning.

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    IEEE Journal of Biomedical and Health Informatics
    |April 6, 2023
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    Convolutional neural networks (CNNs) for myoelectric simultaneous and proportional control (SPC) show performance declines when training and testing conditions vary. Predictive accuracy is sensitive to changes in motion amplitude and frequency, impacting device reliability.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Neuroscience

    Background:

    • Myoelectric control systems translate muscle electrical activity into device commands.
    • Convolutional neural networks (CNNs) show promise for improving myoelectric simultaneous and proportional control (SPC).
    • Variability in user movement (amplitude, frequency) can affect the performance of trained control algorithms.

    Purpose of the Study:

    • To investigate the impact of differing training and testing conditions on CNN predictions for myoelectric SPC.
    • To quantify performance changes due to variations in motion amplitude and frequency.
    • To understand the underlying mechanisms causing prediction discrepancies.

    Main Methods:

    • Utilized a dataset of electromyogram (EMG) signals and joint angular accelerations from a star-drawing task.
    • Trained CNNs on specific motion amplitude/frequency combinations and tested on others.
    • Assessed prediction accuracy using normalized root mean squared error (NRMSE), correlation, and linear regression slope.

    Main Results:

    • Predictive performance degraded differently based on whether amplitude/frequency increased or decreased between training and testing.
    • Correlations decreased with factor reduction; slopes deteriorated with factor increase.
    • NRMSE worsened in both increasing and decreasing factor scenarios, with greater impact from decreases.

    Conclusions:

    • CNN performance in myoelectric SPC is sensitive to variations in motion amplitude and frequency.
    • Signal-to-noise ratio (SNR) changes and limited prediction range likely contribute to performance degradation.
    • Findings suggest avenues for developing robust myoelectric SPC systems resilient to environmental variability.