YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Biomechanical Engineering
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Biomechanical Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Slow-Time Changes in Human EMG Muscle Fatigue States Are Fully Represented in Movement Kinematics

    Source: Journal of Biomechanical Engineering:;2009:;volume( 131 ):;issue: 002::page 21004
    Author:
    Miao Song
    ,
    Jonathan B. Dingwell
    ,
    David Chelidze
    ,
    David B. Segala
    DOI: 10.1115/1.3005177
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The ability to identify physiologic fatigue and related changes in kinematics can provide an important tool for diagnosing fatigue-related injuries. This study examined an exhaustive cycling task to demonstrate how changes in movement kinematics and variability reflect underlying changes in local muscle states. Motion kinematics data were used to construct fatigue features. Their multivariate analysis, based on smooth orthogonal decomposition, was used to reconstruct physiological fatigue. Two different features composed of (1) standard statistical metrics (SSM), which were a collection of standard long-time measures, and (2) phase space warping (PSW)–based metrics, which characterized short-time variations in the phase space trajectories, were considered. Movement kinematics and surface electromyography (EMG) signals were measured from the lower extremities of seven highly trained cyclists as they cycled to voluntary exhaustion on a stationary bicycle. Mean and median frequencies from the EMG time series were computed to measure the local fatigue dynamics of individual muscles independent of the SSM- and PSW-based features, which were extracted solely from the kinematics data. A nonlinear analysis of kinematic features was shown to be essential for capturing full multidimensional fatigue dynamics. A four-dimensional fatigue manifold identified using a nonlinear PSW-based analysis of kinematics data was shown to adequately predict all EMG-based individual muscle fatigue trends. While SSM-based analyses showed similar dominant global fatigue trends, they failed to capture individual muscle activities in a low-dimensional manifold. Therefore, the nonlinear PSW-based analysis of strictly kinematic time series data directly predicted all of the local muscle fatigue trends in a low-dimensional systemic fatigue trajectory. These results provide the first direct quantitative link between changes in muscle fatigue dynamics and resulting changes in movement kinematics.
    keyword(s): Fatigue , Electromyography , Muscle , Kinematics , Time series AND Dynamics (Mechanics) ,
    • Download: (447.2Kb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Slow-Time Changes in Human EMG Muscle Fatigue States Are Fully Represented in Movement Kinematics

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/140014
    Collections
    • Journal of Biomechanical Engineering

    Show full item record

    contributor authorMiao Song
    contributor authorJonathan B. Dingwell
    contributor authorDavid Chelidze
    contributor authorDavid B. Segala
    date accessioned2017-05-09T00:31:49Z
    date available2017-05-09T00:31:49Z
    date copyrightFebruary, 2009
    date issued2009
    identifier issn0148-0731
    identifier otherJBENDY-26876#021004_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/140014
    description abstractThe ability to identify physiologic fatigue and related changes in kinematics can provide an important tool for diagnosing fatigue-related injuries. This study examined an exhaustive cycling task to demonstrate how changes in movement kinematics and variability reflect underlying changes in local muscle states. Motion kinematics data were used to construct fatigue features. Their multivariate analysis, based on smooth orthogonal decomposition, was used to reconstruct physiological fatigue. Two different features composed of (1) standard statistical metrics (SSM), which were a collection of standard long-time measures, and (2) phase space warping (PSW)–based metrics, which characterized short-time variations in the phase space trajectories, were considered. Movement kinematics and surface electromyography (EMG) signals were measured from the lower extremities of seven highly trained cyclists as they cycled to voluntary exhaustion on a stationary bicycle. Mean and median frequencies from the EMG time series were computed to measure the local fatigue dynamics of individual muscles independent of the SSM- and PSW-based features, which were extracted solely from the kinematics data. A nonlinear analysis of kinematic features was shown to be essential for capturing full multidimensional fatigue dynamics. A four-dimensional fatigue manifold identified using a nonlinear PSW-based analysis of kinematics data was shown to adequately predict all EMG-based individual muscle fatigue trends. While SSM-based analyses showed similar dominant global fatigue trends, they failed to capture individual muscle activities in a low-dimensional manifold. Therefore, the nonlinear PSW-based analysis of strictly kinematic time series data directly predicted all of the local muscle fatigue trends in a low-dimensional systemic fatigue trajectory. These results provide the first direct quantitative link between changes in muscle fatigue dynamics and resulting changes in movement kinematics.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleSlow-Time Changes in Human EMG Muscle Fatigue States Are Fully Represented in Movement Kinematics
    typeJournal Paper
    journal volume131
    journal issue2
    journal titleJournal of Biomechanical Engineering
    identifier doi10.1115/1.3005177
    journal fristpage21004
    identifier eissn1528-8951
    keywordsFatigue
    keywordsElectromyography
    keywordsMuscle
    keywordsKinematics
    keywordsTime series AND Dynamics (Mechanics)
    treeJournal of Biomechanical Engineering:;2009:;volume( 131 ):;issue: 002
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian