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Related Experiment Video

Updated: Oct 22, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Recognizing Missing Electromyography Signal by Data Split Reorganization Strategy and Weight-Based Multiple Neural

Feng Duan, Yikang Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 30, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a data split reorganization (DSR) and weight-based multiple neural network voting (WMV) method to improve surface electromyography (sEMG) signal recognition for prosthetic hands, even with missing data.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Engineering
    • Signal Processing

    Background:

    • Surface electromyography (sEMG) signals are crucial for prosthetic hand control.
    • Wireless sEMG acquisition can lead to signal loss, impacting gesture recognition accuracy.
    • Existing methods for handling missing sEMG data can be inaccurate and computationally expensive.

    Purpose of the Study:

    • To propose and validate novel methods for recognizing hand gestures from sEMG signals with missing data.
    • To address the challenge of slight to moderate signal loss in sEMG-based prosthetic control.
    • To improve the accuracy and efficiency of sEMG signal processing for prosthetic applications.

    Main Methods:

    • A data split reorganization (DSR) strategy was developed to maximize the utility of available sEMG features.
    • A weight-based multiple neural network voting (WMV) method was employed for gesture recognition.
    • Controllable missing sEMG signals were artificially generated for robust validation.
    • Three time-domain features were extracted using non-overlapping sliding windows.

    Main Results:

    • The proposed DSR and WMV methods achieved high accuracy in recognizing hand gestures with missing sEMG data.
    • Accuracies of 93.66%, 92.55%, and 91.19% were recorded for 5%, 10%, and 15% data missing ratios, respectively.
    • Statistical analysis confirmed the significant superiority of the proposed methods over baseline approaches.

    Conclusions:

    • The DSR strategy combined with WMV effectively handles missing sEMG data for prosthetic hand gesture recognition.
    • The proposed methods offer a significant improvement in accuracy for sEMG-based control systems.
    • Future work will focus on optimizing these methods for recognizing more severe sEMG signal loss.