Tracking or predicting physiological fatigue is important for developing more robust training protocols and better energy supplements and/or reducing muscle injuries. Current methodologies are usually impractical and/or invasive and may not be realizable outside of laboratory settings. It was recently demonstrated that smooth orthogonal decomposition (SOD) of phase space warping (PSW) features of motion kinematics can identify fatigue in individual muscle groups. We hypothesize that a nonlinear extension of SOD will identify more optimal fatigue coordinates and provide a lower-dimensional reconstruction of local fatigue dynamics than the linear SOD. Both linear and nonlinear SODs were applied to PSW features estimated from measured kinematics to reconstruct muscle fatigue dynamics in subjects performing a sawing motion. Ten healthy young right-handed subjects pushed a weighted handle back and forth until voluntary exhaustion. Three sets of joint kinematic angles were measured from the right upper extremity in addition to surface electromyography (EMG) recordings. The SOD coordinates of kinematic PSW features were compared against independently measured fatigue markers (i.e., mean and median EMG spectrum frequencies of individual muscle groups). This comparison was based on a least-squares linear fit of a fixed number of the dominant SOD coordinates to the appropriate local fatigue markers. Between subject variability showed that at most four to five nonlinear SOD coordinates were needed to reconstruct fatigue in local muscle groups, while on average 15 coordinates were needed for the linear SOD. Thus, the nonlinear coordinates provided a one-order-of-magnitude improvement over the linear ones.
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e-mail: chelidze@egr.uri.edu
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March 2011
Research Papers
Nonlinear Smooth Orthogonal Decomposition of Kinematic Features of Sawing Reconstructs Muscle Fatigue Evolution as Indicated by Electromyography
David B. Segala,
David B. Segala
Nonlinear Dynamics Laboratory, Department of Mechanical, Industrial and Systems Engineering,
University of Rhode Island
, Kingston, RI 02881
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Deanna H. Gates,
Deanna H. Gates
Department of Biomedical Engineering,
University of Texas at Austin
, Austin, TX 78712
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Jonathan B. Dingwell,
Jonathan B. Dingwell
Nonlinear Biodynamics Laboratory, Department of Kinesiology and Health Education,
University of Texas at Austin
, Austin, TX 78712
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David Chelidze
David Chelidze
Nonlinear Dynamics Laboratory, Department of Mechanical, Industrial and Systems Engineering,
e-mail: chelidze@egr.uri.edu
University of Rhode Island
, Kingston, RI 02881
Search for other works by this author on:
David B. Segala
Nonlinear Dynamics Laboratory, Department of Mechanical, Industrial and Systems Engineering,
University of Rhode Island
, Kingston, RI 02881
Deanna H. Gates
Department of Biomedical Engineering,
University of Texas at Austin
, Austin, TX 78712
Jonathan B. Dingwell
Nonlinear Biodynamics Laboratory, Department of Kinesiology and Health Education,
University of Texas at Austin
, Austin, TX 78712
David Chelidze
Nonlinear Dynamics Laboratory, Department of Mechanical, Industrial and Systems Engineering,
University of Rhode Island
, Kingston, RI 02881e-mail: chelidze@egr.uri.edu
J Biomech Eng. Mar 2011, 133(3): 031009 (10 pages)
Published Online: February 8, 2011
Article history
Received:
July 14, 2010
Revised:
December 2, 2010
Posted:
December 22, 2010
Published:
February 8, 2011
Online:
February 8, 2011
Citation
Segala, D. B., Gates, D. H., Dingwell, J. B., and Chelidze, D. (February 8, 2011). "Nonlinear Smooth Orthogonal Decomposition of Kinematic Features of Sawing Reconstructs Muscle Fatigue Evolution as Indicated by Electromyography." ASME. J Biomech Eng. March 2011; 133(3): 031009. https://doi.org/10.1115/1.4003320
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