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Related Experiment Video

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Different sEMG and EEG Features Analysis for Gait phase Recognition.

Pengna Wei, Jinhua Zhang, Pingping Wei

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study accurately identifies human gait phases using surface electromyography (sEMG) and electroencephalography (EEG) signals. The slope sign change (SSC) and mean power frequency (MPF) features show high accuracy, especially at faster walking speeds.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Biomechanics

    Background:

    • Gait phase recognition is crucial for understanding human locomotion and developing advanced assistive technologies.
    • Electromyography (sEMG) and electroencephalography (EEG) signals offer potential for non-invasive gait analysis.

    Purpose of the Study:

    • To evaluate the effectiveness of various sEMG and EEG features for accurate gait phase recognition.
    • To determine the optimal walking speed for gait phase classification using these biosignals.

    Main Methods:

    • Seven healthy volunteers performed treadmill walking at three different speeds (1.4, 2.0, and 2.6 km/h).
    • Lower limb 3D trajectories were used to define seven distinct gait phases.
    • Surface electromyography (sEMG) and electroencephalography (EEG) signals were recorded and analyzed using specific features, including slope sign change (SSC) and mean power frequency (MPF), with classification performed via Library for Support Vector Machines (LIBSVM).

    Main Results:

    • The slope sign change (SSC) and mean power frequency (MPF) of sEMG signals, along with the SSC of EEG signals, demonstrated superior accuracy in gait phase recognition compared to other assessed features.
    • Recognition accuracies reached 95.58% at 1.4 km/h, 97.63% at 2.0 km/h, and 98.10% at 2.6 km/h.
    • Higher walking speeds, particularly 2.6 km/h, yielded improved gait phase recognition accuracy.

    Conclusions:

    • sEMG and EEG signal features, specifically SSC and MPF, are highly effective for accurate gait phase recognition.
    • Treadmill walking at faster speeds enhances the precision of gait phase classification using these biosignal features.