Neural drive estimation using the hypothesis of muscle synergies and the state-constrained Kalman filter

Ghulam Rasool, Kamran Iqbal, Nidhal Bouaynaya, Gannon White

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

    6 Scopus citations

    Abstract

    We explore the hypothesis of muscle synergies to estimate the neural drive (movement intent) for upper extremity myoelectric prosthesis using the surface myoelectric signals. Commonly employed pattern classification systems have certain limitations, like inherent discrete nature, finite movement classes and limited degrees-of-freedom. We propose a novel framework based on the state space modeling and the hypothesis of muscle synergies. The problem is formulated in the state space framework in a novel way, where the movement intent is modeled as the hidden state of the system. A continuous stream of the movement intent (the hidden state) is estimated using the state-constrained Kalman filter. Preliminary experimental results also confirm the applicability of the proposed framework for estimation of movement intent.

    Original languageEnglish (US)
    Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
    Pages802-805
    Number of pages4
    DOIs
    StatePublished - Dec 1 2013
    Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
    Duration: Nov 6 2013Nov 8 2013

    Publication series

    NameInternational IEEE/EMBS Conference on Neural Engineering, NER
    ISSN (Print)1948-3546
    ISSN (Electronic)1948-3554

    Other

    Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
    Country/TerritoryUnited States
    CitySan Diego, CA
    Period11/6/1311/8/13

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Mechanical Engineering

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