Characteristics of Surface EMG During Gait with and Without Power Assistance

Seiji Saito, Satoshi Muraki

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

    Abstract

    Technology that assists and extends various functions of human beings will soon be available not only to medical and welfare but also to healthy individuals. This study aimed to characterize surface electromyography (EMG) signals in response to walking assistive equipment. Ten healthy male students walked on an 8-m uphill road (5.8% incline) using an assist walker (RT.2, RT.WORKS Co., Ltd) under assist and non-assist conditions. The EMG signals were recorded from four muscles (the rectus femoris [RF], biceps femoris, tibialis anterior, and lateral gastrocnemius). During loading response and terminal stance, the percent maximum voluntary isometric contraction (%MVC) peak value for RF was achieved more quickly in the assist condition than in the non-assist condition. However, during loading response and mid-swing, the %MVC peak value of RF was significantly lower in the assist condition than in the non-assist condition. These results indicate that humans alter muscle exertion patterns in specific muscles to adapt to walking assistance; such a change in the muscle exertion pattern may be adapted for smoother walking.

    Original languageEnglish
    Title of host publicationProceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume II
    Subtitle of host publicationSafety and Health, Slips, Trips and Falls
    EditorsYushi Fujita, Sebastiano Bagnara, Thomas Alexander, Riccardo Tartaglia, Sara Albolino
    PublisherSpringer Verlag
    Pages739-743
    Number of pages5
    ISBN (Print)9783319960883
    DOIs
    Publication statusPublished - 2019
    Event20th Congress of the International Ergonomics Association, IEA 2018 - Florence, Italy
    Duration: Aug 26 2018Aug 30 2018

    Publication series

    NameAdvances in Intelligent Systems and Computing
    Volume819
    ISSN (Print)2194-5357

    Other

    Other20th Congress of the International Ergonomics Association, IEA 2018
    Country/TerritoryItaly
    CityFlorence
    Period8/26/188/30/18

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • General Computer Science

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