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Related Experiment Video

Updated: Oct 10, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

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Optimizing Input for Gesture Recognition using Convolutional Networks on HD-sEMG Instantaneous Images.

Michael Houston, Albon Wu, Yingchun Zhang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    For hand gesture recognition, using high-density surface electromyography (HD-sEMG) with convolutional neural networks (CNNs), baseline monopolar signals yield higher accuracy than pre-processed signals. This suggests avoiding extra preprocessing steps for optimal performance.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Signal Processing

    Background:

    • High-density surface electromyography (HD-sEMG) offers high spatio-temporal resolution for hand gesture recognition.
    • Convolutional neural networks (CNNs) are increasingly used to analyze HD-sEMG data for feature extraction.

    Purpose of the Study:

    • To investigate whether common pre-processing techniques improve classification accuracy of HD-sEMG signals for gesture recognition when using CNNs.
    • To compare the performance of different spatial filtering configurations on HD-sEMG data.

    Main Methods:

    • Applied common pre-processing techniques (monopolar, bipolar, rectified, common-average referenced, Laplacian) to a benchmark HD-sEMG dataset (CapgMyo DB-a).
    • Evaluated classification accuracies using CNNs on instantaneous HD-sEMG signal samples for each configuration.

    Main Results:

    • Baseline monopolar HD-sEMG signals demonstrated higher prediction accuracies compared to other pre-processed signal configurations.
    • No significant improvement in classification accuracy was observed with additional pre-processing steps.

    Conclusions:

    • The study discourages the use of extra pre-processing steps when employing CNNs for classifying instantaneous HD-sEMG samples for gesture recognition.
    • Monopolar HD-sEMG signals are sufficient and potentially optimal for CNN-based gesture recognition.