An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions

J Electromyogr Kinesiol. 2005 Apr;15(2):210-21. doi: 10.1016/j.jelekin.2004.08.008. Epub 2004 Nov 18.

Abstract

During a sustained contraction, electromyographic signals (EMGs) undergo a spectral compression. This fatigue behaviour induces a shift of the mean and the median frequencies to lower frequencies. On the other hand, several studies conclude that the mean/median frequency can increase, decrease or remain constant with an increasing force level. Such inconsistency is embarrassing since the fatigue state may be influenced by the force level. In this paper, we propose a frequency indicator which is sensitive to the force level independently of the fatigue state evaluated at 70% of the maximal voluntary contraction. Ten healthy volunteers participated in the study and both surface EMGs (from the short head of the biceps brachii) and force signals were measured. This study compared force and fatigue effects on the EMGs during short (3-s) isometric contractions at different strength intensities and during a sustained isometric contraction until exhaustion. The EMGs partly show 1/falpha spectral behaviours since their power spectral densities may experimentally fit with two linear segments in a log-log representation. The measured "right" slope produces variations of force as 20 times the variations of fatigue. 1/falpha Behaviour may be related to stochastic fractals. This fractal indicator is a new frequency indicator that is thus complementary to other known classical frequency indicators when studying force during unknown fatigue states.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Analog-Digital Conversion
  • Arm / physiology
  • Elbow Joint / physiology
  • Electromyography / statistics & numerical data*
  • Feedback
  • Female
  • Fractals*
  • Humans
  • Isometric Contraction / physiology*
  • Male
  • Muscle Fatigue / physiology*
  • Muscle, Skeletal / physiology*
  • Signal Processing, Computer-Assisted
  • Stochastic Processes
  • Stress, Mechanical
  • Time Factors