EMG-Based Volitional Torque Estimation in Functional Electrical Stimulation Control

Hossein Kavianirad*, Satoshi Endo, Thierry Keller, Sandra Hirche

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Citations (Scopus)

    Abstract

    Functiona1 electrical stimulation (FES) applies electrical pulses to muscle fibers through the skin for assisting functional movements in patients with motor disability. Muscle activity feedback such as volitional Electromyography (vEMG) can optimize the performance of the FES system in both rehabilitation or activity of daily living (ADL), however, artifacts caused by simultaneous use of FES and EMG on the same muscles contaminate the EMG signal. This paper, using an adaptive filter, aims to investigate the estimation of the volitional torque from filtered vEMG. Based on this estimation, the usability and performance of the adaptive filter for estimating volitional torque are studied on 5 healthy participants and we show that this filter can be used for volitional torque estimation. In the next step, it is shown how this map can be used in closed-loop FES control for estimating volitional torque.

    Original languageEnglish
    Title of host publication7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages171-176
    Number of pages6
    ISBN (Electronic)9781665494694
    DOIs
    Publication statusPublished - 2022
    Event7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings - Virtual, Online, Malaysia
    Duration: 7 Dec 20229 Dec 2022

    Publication series

    Name7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings

    Conference

    Conference7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings
    Country/TerritoryMalaysia
    CityVirtual, Online
    Period7/12/229/12/22

    Keywords

    • closed-loop control
    • EMG
    • FES
    • human-in-the-loop
    • human-machine interaction
    • rehabilitation

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