EMG Acquisition and Hand Pose Classification for Bionic Hands from Randomly-placed Sensors

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    This paper presents a unique real-time motion recognition system for Electromyographic (EMG) signal acquisition and classification. It is the first approach which can classify hand poses from multi-channel EMG signals gathered from randomly placed arm sensors as accurately as current placed-sensor EMG acquisition approaches. It combines time-domain feature extraction, Linear Discriminant Analysis (LDA) feature projection and Multilayer Perceptron (MLP) classification to allow nine distinct poses to be correctly identified more than 95% of the time. This is comparable to state-of-the-art placed-sensor EMG acquisition systems. Processing times of 11.70 ms also make this a viable candidate approach for real-time EMG acquisition and processing in practical prosthesis applications.

    Documents

    DOI

    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings
    Publisher IEEE
    Pages1105-1109
    Number of pages5
    DOIs
    Publication statusPublished - 13 Sep 2018
    EventIEEE International Conference on Acoustics, Speech and Signal Processing - Calgary, Calgary, Canada
    Duration: 15 Jan 201820 Jan 2018
    Conference number: 2018
    https://2018.ieeeicassp.org/
    https://2018.ieeeicassp.org

    Publication series

    NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings
    PublisherIEEE
    ISSN (Electronic)2379-190X

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
    Abbreviated titleICASSP
    CountryCanada
    CityCalgary
    Period15/01/201820/01/2018
    Internet address

      Research areas

    • Electromyographic (EMG), time-domain features, pattern recognition, linear discriminant analysis (LDA), multilayer perceptron (MLP).

    ID: 157855322