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Determination of Muscle Fatigue Using Dynamically Embedded Signals

[+] Author Affiliations
Paul S. Slack, Xianghong Ma

Aston University, Birmingham, UK

Paper No. DETC2007-34287, pp. 1565-1574; 10 pages
  • ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5: 6th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C
  • Las Vegas, Nevada, USA, September 4–7, 2007
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4806-X | eISBN: 0-7918-3806-4
  • Copyright © 2007 by ASME


There is concern associated with the duration that a microsurgeon operates. Muscle fatigue can present itself over time and adversely affect the surgeon’s ability to perform appropriately during lengthy procedures. This paper explores a new method of analyzing muscle fatigue within the muscles predominantly used during micro-surgery. The captured Electro-MyoGraphic (EMG) data retrieved from these muscles are analyzed for any defining patterns relating to muscle fatigue. The analysis consists of dynamically embedding the EMG signals from a single muscle channel into an embedded matrix. The muscle fatigue is determined by defining its entropy characterized by the singular values of the Dynamical Embedded (DE) matrix. The paper compares this new method with the traditional method of using mean frequency shifts in EMG signal’s power spectral density. Linear regressions are fitted to the results from both methods, and the coefficient of variation of both their slope and point of intercept are determined. It is shown that the complexity method is more robust in that the coefficient of variation for the DE method has lower variability than the conventional method of mean frequency analysis.

Copyright © 2007 by ASME
Topics: Fatigue , Muscle , Signals



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