Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    8 Citations (Scopus)

    Abstract

    Electromyography (EMG) signal analysis is one of the key determinants of the effectiveness of prosthetic devices. Modern researchers provide various methods of detection of different hand movements and postures. In this work, we examined the possibility to produce efficient detection of hand movement to a specific posture with the minimum possible number of electrodes. The data acquisition is produced with 1 channel BiTalino EMG sensor based on bipolar differential measurement. Using feature extraction and artificial neural network we achieved 82% of offline classification accuracy for 8 hand motions and 91% accuracy for 6 hand motions based on 200 ms of EMG signal. Also, the motion detection algorithm was developed and successfully tested that allowed to implement the algorithm for real-time classification and that showed sufficient accuracy for 2 and 4 motion classes cases.

    Original languageEnglish
    Title of host publication2018 6th International Conference on Brain-Computer Interface, BCI 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-5
    Number of pages5
    Volume2018-January
    ISBN (Electronic)9781538625743
    DOIs
    Publication statusPublished - Mar 9 2018
    Event6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of
    Duration: Jan 15 2018Jan 17 2018

    Conference

    Conference6th International Conference on Brain-Computer Interface, BCI 2018
    CountryKorea, Republic of
    CityGangWon
    Period1/15/181/17/18

    Keywords

    • ANN
    • BiTalino
    • Blender
    • EMG
    • Hand
    • posture detection

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Human-Computer Interaction
    • Behavioral Neuroscience

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  • Cite this

    Oleinikov, A., Abibullaev, B., Shintemirov, A., & Folgheraiter, M. (2018). Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (Vol. 2018-January, pp. 1-5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311527