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Gesture Recognition Through Mechanomyogram Signals: An Adaptive Framework for Arm Posture Variability.

Panipat Wattanasiri, Samuel Wilson, Weiguang Huo

    IEEE Journal of Biomedical and Health Informatics
    |October 28, 2024
    PubMed
    Summary

    This study introduces a new mechanomyogram (MMG) device for hand gesture recognition, overcoming arm posture challenges with unsupervised domain adaptation. The system achieves high accuracy, offering a promising alternative to traditional electromyogram (EMG) methods.

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    Area of Science:

    • Biomedical Engineering
    • Human-Computer Interaction
    • Signal Processing

    Background:

    • Hand gesture recognition is crucial for human-computer interaction.
    • Classifying gestures across varying arm postures presents significant challenges due to dynamic muscle activity.
    • Existing methods often rely on electromyogram (EMG) sensors requiring skin contact.

    Purpose of the Study:

    • To develop a robust hand gesture recognition system that addresses arm posture variability.
    • To utilize a wearable mechanomyogram (MMG) device, eliminating the need for electrical skin contact.
    • To evaluate the effectiveness of unsupervised domain adaptation for improving gesture classification accuracy across different postures.

    Main Methods:

    • Employed a wearable mechanomyogram (MMG) device for muscle activity sensing.
    • Utilized Continuous Wavelet Transform (CWT) for feature extraction from MMG signals.
    • Implemented Domain-Adversarial Convolutional Neural Networks (DACNN) with unsupervised domain adaptation for gesture classification.
    • Compared DACNN performance against supervised classifiers across multiple arm postures.

    Main Results:

    • The proposed DACNN method demonstrated consistent improvement in classification accuracy compared to supervised methods across various arm postures.
    • Achieved an average prediction accuracy of 87.43% for intra-posture and 64.29% for inter-posture classification of 5 hand gestures.
    • Expanding the MMG segmentation window to 600 ms increased intra-posture accuracy to 92.32% and inter-posture accuracy to 71.75%.

    Conclusions:

    • The developed method effectively improves gesture recognition generalization across dynamic arm posture changes.
    • Mechanomyogram (MMG) shows potential as a viable alternative sensor for gesture recognition, comparable to electromyogram (EMG).
    • The system is suitable for non-laboratory usage, offering a user-friendly setup and high performance.