Slow-time changes in human EMG muscle fatigue states are fully represented in movement kinematics.
Slow-time changes in human EMG muscle fatigue states are fully represented in movement kinematics.
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DOI：
10.1115/1.3005177
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发表时间：
2009-03
影响因子：
1.7
通讯作者：
Chelidze, David
中科院分区：
文献类型：
--
作者：
Song, Miao;Segala, David B.;Dingwell, Jonathan B.;Chelidze, David
The ability to identify physiologic fatigue and related changes in kinematics can provide 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 10 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 kinematic data. Nonlinear analysis of kinematic features was shown to be essential for capturing full multidimensional fatigue dynamics. A four-dimensional fatigue manifold identified using nonlinear PSW-based analysis of kinematic 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 activity in a low dimensional manifold. Therefore, nonlinear PSW-based analysis of strictly kinematic time series data directly predicted all of the local muscle fatigue trends in a low-dimensional systemic global fatigue trajectory. These results provide the first direct quantitative link between changes in muscle fatigue dynamics and resulting changes in movement kinematics.
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影响因子：
2.5
作者：
Ebaugh, DD;McClure, PW;Karduna, AR
通讯作者：
Karduna, AR
DOI：
10.1115/1.1456908
发表时间：
2002-04-01
影响因子：
1.7
作者：
Chelidze, D;Cusumano, JP;Chatterjee, A
通讯作者：
Chatterjee, A
影响因子：
4.7
作者：
Chelidze, D;Zhou, WL
通讯作者：
Zhou, WL
DOI：
10.1098/rsta.2006.1837
发表时间：
2006-09-15
影响因子：
5
作者：
Chelidze, D.;Cusumano, J. P.
通讯作者：
Cusumano, J. P.
影响因子：
4.8
作者：
Dannenberg, AL;Needle, S;Kolodner, KB
通讯作者：
Kolodner, KB