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    This study improves electromyography (EMG)-based gesture recognition by using rotation augmentation and domain adaptation to overcome challenges caused by electrode shifts. The new method enhances accuracy and adaptability for real-world applications, reducing recalibration needs.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Human-Computer Interaction

    Background:

    • Electromyography (EMG)-based gesture recognition is vital for prosthetics and human-computer interfaces.
    • Real-world application is hindered by sensitivity to electrode placement variations.
    • Existing methods struggle with performance degradation due to shifts in sensor positioning.

    Purpose of the Study:

    • To develop a robust EMG-based gesture recognition method resilient to changes in electrode wearing position.
    • To enhance the generalizability and adaptability of EMG systems for practical use.
    • To reduce the necessity for frequent recalibration in dynamic environments.

    Main Methods:

    • Integration of rotation augmentation to synthetically generate varied electrode placements.
    • Application of unsupervised adversarial domain adaptation to align feature distributions.
    • Validation using Ninapro-DB5 and a newly collected LGEST-DB1 dataset with controlled positional variations.

    Main Results:

    • Rotation augmentation and domain adaptation synergistically improved recognition accuracy.
    • Achieved a 31.93% accuracy gain on LGEST-DB1 and 13.54% on Ninapro-DB5 compared to baseline.
    • Demonstrated significant enhancement in adaptability for real-world EMG gesture recognition.

    Conclusions:

    • The proposed method effectively addresses the challenge of electrode shift sensitivity in EMG gesture recognition.
    • Combining rotation augmentation and domain adaptation offers a powerful solution for robust real-world deployment.
    • The approach significantly reduces recalibration needs, paving the way for more practical EMG-based systems.