EMG wrist-hand motion recognition system for real-time Embedded platform

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    Electromyography (EMG) signal analysis is a popular method for controlling prosthetic and gesture control equipment. For portable systems, such as prosthetic limbs, real-time low-power operation on embedded processors is critical, but to date there has been no record of how existing EMG analysis approaches support such deployments. This paper presents a novel approach to time-domain classification of multichannel EMG signals harnessed from randomly-placed sensors according to the wrist-hand movements which caused their occurrence. It shows how, by employing a very small set of time-domain features, Kernel Fisher discriminant feature projection and Radial Bias Function neural network classifiers, nine wrist-hand movements can be detected with accuracy exceeding 99% - surpassing the state-of-the-art on record. It also shows how, when deployed on ARM Cortex-A53, the processing time is not only sufficient to enable real-time processing but is also a factor 50 shorter than the leading time-frequency techniques on record.

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    DOI

    Original languageEnglish
    Title of host publication2019 International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
    Publisher IEEE
    Pages1523-1527
    ISBN (Electronic)978-1-4799-8131-1
    ISBN (Print)978-1-4799-8132-8
    DOIs
    Publication statusPublished - 17 Apr 2019

    ID: 164582148