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    This study reveals that analysis window length and electrode count significantly impact electromyography (EMG) pattern recognition for prosthetic control. Window overlap has no effect, and partial-hand amputees show promising accuracy.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Engineering
    • Signal Processing

    Background:

    • Advanced prosthetic devices rely on electromyography (EMG) signal classification for gesture recognition.
    • Classification accuracy is crucial for effective real-time control of prosthetic limbs.

    Purpose of the Study:

    • To investigate the influence of temporal and spatial information on EMG classifier performance.
    • To analyze the inter-dependencies of analysis window length, window overlap, and electrode channels on classification accuracy.

    Main Methods:

    • Recorded EMG data for seven hand gestures from partial-hand amputees, trans-radial amputees, and able-bodied individuals.
    • Conducted an extensive investigation into analysis window length, window overlap, and the number of electrode channels.
    • Analyzed the interactions between these parameters and their effect on classification accuracy.

    Main Results:

    • Classification accuracy is largely independent of electrode count concerning analysis window length.
    • Window overlap does not influence classifier performance across different parameters and limb conditions.
    • Limb deficiency type and channel count affect error reduction when adding more channels.
    • Partial-hand amputees achieved classification accuracies close to able-bodied volunteers.

    Conclusions:

    • Optimizing analysis window length and electrode configuration is key for improving EMG-based prosthetic control.
    • Window overlap is not a critical factor for enhancing classification accuracy.
    • Prosthetic control shows potential for partial-hand amputees, with performance nearing that of able-bodied users.