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Holding Static Arm Configurations With Functional Electrical Stimulation: A Case Study

    • Cleveland State University

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Functional electrical stimulation (FES) is a promising solution for restoring functional motion to individuals with paralysis, but the potential for achieving any desired full-arm reaching motion has not been realized. We present a combined feed-forward-feedback controller capable of automatically calculating and applying the necessary muscle stimulations to hold the wrist of an individual with high tetraplegia in a desired static position. We used the controller to hold a complete arm configuration to maintain a series of static wrist positions. The average distance to the target wrist position, or accuracy, was 2.9 cm. The precision is defined as the radius of the 95% confidence ellipsoid for the final positions of a set of trials with the same muscle stimulations and starting position. The average precision was 3.7 cm. The control architecture used in this study to hold static positions has the potential to control arbitrary reaching motions.

    Original languageAmerican English
    JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
    Volume26
    DOIs
    StatePublished - Oct 1 2018

    Keywords

    • Machine learning; spinal cord injuries (SCI); neuroprosthesis; upper extremity; system identification; tetraplegia; quadriplegia; rehabilitation robotics; model learning

    Disciplines

    • Biomechanical Engineering
    • Robotics

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