Literature DB >> 15664150

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

Philippe Ravier1, Olivier Buttelli, Rachid Jennane, Pierre Couratier.   

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.

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Year:  2004        PMID: 15664150     DOI: 10.1016/j.jelekin.2004.08.008

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  10 in total

1.  Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations.

Authors:  Gürkan Bilgin; I Ethem Hindistan; Y Gül Özkaya; Etem Köklükaya; Övünç Polat; Ömer H Çolak
Journal:  J Med Syst       Date:  2015-08-15       Impact factor: 4.460

Review 2.  Is fatigue all in your head? A critical review of the central governor model.

Authors:  J P Weir; T W Beck; J T Cramer; T J Housh
Journal:  Br J Sports Med       Date:  2006-07       Impact factor: 13.800

3.  The effect of single-pulse transcranial magnetic stimulation and peripheral nerve stimulation on complexity of EMG signal: fractal analysis.

Authors:  M Cukic; J Oommen; D Mutavdzic; N Jorgovanovic; M Ljubisavljevic
Journal:  Exp Brain Res       Date:  2013-05-08       Impact factor: 1.972

4.  Changes in muscle activity and kinematics of highly trained cyclists during fatigue.

Authors:  Jonathan B Dingwell; Jason E Joubert; Fernando Diefenthaeler; Joel D Trinity
Journal:  IEEE Trans Biomed Eng       Date:  2008-11       Impact factor: 4.538

5.  Novel feature modelling the prediction and detection of sEMG muscle fatigue towards an automated wearable system.

Authors:  Mohamed R Al-Mulla; Francisco Sepulveda
Journal:  Sensors (Basel)       Date:  2010-05-12       Impact factor: 3.576

Review 6.  A review of non-invasive techniques to detect and predict localised muscle fatigue.

Authors:  Mohamed R Al-Mulla; Francisco Sepulveda; Martin Colley
Journal:  Sensors (Basel)       Date:  2011-03-24       Impact factor: 3.576

7.  Effects of Force Load, Muscle Fatigue, and Magnetic Stimulation on Surface Electromyography during Side Arm Lateral Raise Task: A Preliminary Study with Healthy Subjects.

Authors:  Liu Cao; Ying Wang; Dongmei Hao; Yao Rong; Lin Yang; Song Zhang; Dingchang Zheng
Journal:  Biomed Res Int       Date:  2017-04-11       Impact factor: 3.411

8.  Improving Precision Force Control With Low-Frequency Error Amplification Feedback: Behavioral and Neurophysiological Mechanisms.

Authors:  Ing-Shiou Hwang; Chia-Ling Hu; Zong-Ru Yang; Yen-Ting Lin; Yi-Ching Chen
Journal:  Front Physiol       Date:  2019-02-20       Impact factor: 4.566

9.  Effects of cyclic static stretch on fatigue recovery of triceps surae in female basketball players.

Authors:  M Ghasemi; H Bagheri; G Olyaei; S Talebian; A Shadmehr; S Jalaei; K K Kalantari
Journal:  Biol Sport       Date:  2013-04-11       Impact factor: 2.806

Review 10.  Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses.

Authors:  Iris Kyranou; Sethu Vijayakumar; Mustafa Suphi Erden
Journal:  Front Neurorobot       Date:  2018-09-21       Impact factor: 2.650

  10 in total

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