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Adjacent Features for High-Density EMG Pattern Recognition.

Ian M Donovan, Kazunori Okada, Xiaorong Zhang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    Summary
    This summary is machine-generated.

    A new Adjacent Features (AF) method improves electromyography (EMG) pattern recognition for hand and wrist gestures. This technique enhances classification accuracy compared to traditional methods, offering a simpler computational approach.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Engineering
    • Signal Processing

    Background:

    • Electromyography (EMG) based pattern recognition (PR) has evolved from limited anatomical electrode placements to more adaptable array configurations.
    • Consumer myoelectric control systems now utilize regularly spaced electrodes around limbs, demonstrating effectiveness comparable to traditional methods.
    • While electrode arrays simplify placement, their potential for extracting richer signal information remains an area for exploration.

    Purpose of the Study:

    • To introduce a novel spatial-temporal feature set, Adjacent Features (AF), for analyzing measured electrical signals (MES) in EMG.
    • To evaluate the performance of AF in classifying hand and wrist gestures by analyzing signal intensity, structure, and inter-electrode similarities.
    • To compare the classification accuracy and computational efficiency of AF against established methods like autoregression (AR) and Hudgins' time-domain (TD) features.

    Main Methods:

    • Development of the Adjacent Features (AF) set, which analyzes signal intensity and structure independently.
    • Evaluation of inter-electrode similarities within spatial arrays to capture signal dynamics.
    • Classification of 47 distinct hand and wrist gestures using the proposed AF feature set.

    Main Results:

    • The AF feature set achieved approximately 4%-6% higher classification accuracy compared to AR coefficients and TD features.
    • AF demonstrated comparable computational simplicity to TD features, making it efficient for real-time applications.
    • The novel feature set effectively distinguishes between various hand and wrist gestures.

    Conclusions:

    • Adjacent Features (AF) represent a significant advancement in EMG-based pattern recognition for prosthetic control and human-computer interfaces.
    • The proposed method offers improved accuracy and maintains computational efficiency, making it suitable for practical myoelectric control systems.
    • Further research can explore the application of AF in more complex gesture recognition tasks and diverse EMG signal analysis scenarios.