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Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease
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Correcting Temporal Inaccuracies in Labeled Training Data for Electromyographic Control Algorithms.

Aaron T Wang, Connor D Olsen, W Caden Hamrick

    IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
    |November 9, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Electromyography (EMG) control data is often misaligned due to reaction time. Re-aligning EMG data improves offline performance, but not online performance in real-time control applications.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Rehabilitation Engineering

    Background:

    • Electromyographic (EMG) control systems use supervised learning to interpret motor intent.
    • Training data quality is crucial for EMG control performance.
    • Current methods for collecting EMG data assume perfect synchronization, which is often not the case due to inherent delays.

    Purpose of the Study:

    • To quantify the impact of EMG and kinematic data re-alignment on classification and regression algorithms.
    • To introduce and evaluate a novel trial-by-trial re-alignment method.
    • To assess the effect of re-alignment on both offline and online EMG control performance.

    Main Methods:

    • Compared global cross-correlation re-alignment with a new trial-by-trial re-alignment method.
    • Evaluated performance of classification and regression algorithms with and without human-in-the-loop.
    • Analyzed EMG and kinematic data for inherent misalignments and reaction time inconsistencies.

    Main Results:

    • EMG and kinematic data exhibit inherent misalignments and inconsistent reaction times.
    • Both global and trial-by-trial re-alignment significantly improved offline classification and regression performance.
    • Trial-by-trial re-alignment outperformed global re-alignment in offline classification.
    • No significant difference in online performance was observed with or without re-alignment.

    Conclusions:

    • Labeled EMG data contains inaccuracies that affect algorithm performance.
    • Re-alignment techniques can improve offline EMG control model training.
    • The effectiveness of re-alignment on online performance requires further investigation for real-world EMG control applications.