Cescon, C.

Gazzoni, Marco

Merletti, Roberto

Mesin, Luca

Rainoldi, A.

English

PORTO Publications Open Repository TOrino

A NEW METHOD TO ESTIMATE MYOELECTRIC MANIFESTATION OF MUSCLE 
FATIGUE 
Mesin Luca, Cescon Corrado, Gazzoni Marco, Merletti Roberto*, LISiN, Dip. di Elettronica, 
Politecnico di Torino, Torino, Italy; Rainoldi Alberto, Motor Science Research Center, School 
of Motor Sciences, Università di Torino, Torino, Italy and LISiN, Dip. di Elettronica, 
Politecnico di Torino, Torino, Italy  
* Corresponding author, email: roberto.merletti@polito.it  
 
Aims 
Fatigue may be described as a feeling or sensation of weakness or muscle pain or a 
decrement of performance, not easily suitable for quantification or measurement. During a 
muscle contraction, albeit in absence of mechanical manifestations of fatigue, strong 
modifications in surface EMG signal can occur: the so-called myoelectric manifestations of 
fatigue. Two main physiological factors are at the origin of the myoelectric manifestations of 
fatigue: 1) the decrease of the conduction velocity (CV) of motor unit action potentials 
(MUAP) (“peripheral fatigue” in the following), and 2) the increase of motor unit (MU) 
synchronization by the central nervous system (“central fatigue” in the following). We 
suggest quantifying the myoelectric manifestations of muscle fatigue through a two 
dimensional vector based on two distinct measures of central and peripheral fatigue, instead 
of using a scalar estimator. 
Methods 
To separately describe the peripheral and central components of the myoelectric 
manifestations of fatigue, we investigated the following estimators: 1) mean spectral 
frequency – MNF, 2) median spectral frequency – MDF, 3) root mean square – RMS, 4) 
average rectified value – ARV, 5) muscle fibre conduction velocity - CV, 6) percentage of 
determinism (obtained from recurrence quantification analysis) – %DET , 7) spectral indexes 
defined as the ratio between the signal spectral moment of order -1 and moments of order 
k=2÷5 (FIk), 8) mean frequency of the power spectrum density estimated by autoregressive 
analysis – MNFAR, 9) mean frequency of the power spectrum density estimated by Choi-
Williams time-frequency representation – MNFCWD, 10) mean frequency of the power 
spectrum density estimated by continuous wavelet transform – MNFCWT, 11) fractal 
dimension – FD (the new parameter herein proposed for EMG fatigue estimation).   
The estimators were tested with a set of 20 synthetic signals 20s long using a model with a 
planar description of the volume conductor. The simulated contraction level was 80% MVC. 
Each signal presented an increasing level of synchronization (from 0 to 20% of synchronized 
MUs) and a decreasing CV (-0.1 m/s2). The estimators were calculated on epochs of 0.5s. 
The best estimators were chosen by the criterion of being selective to one of the two 
physiological factors inducing myoelectric manifestation of fatigue, i.e. either to peripheral or 
to central fatigue. 
Results 
It was found that CV estimation is uncorrelated to the level of simulated synchronism (as 
expected), being the most promising method for the estimation of peripheral fatigue. On the 
contrary FD was the estimator least affected by CV changes and most related to the level of 
synchronism, being the most promising estimator of this manifestation of central fatigue. 
Conclusions 
The availability of two independent estimators of central and peripheral fatigue provides new 
insights into myoelectric manifestation of fatigue describing the whole phenomenon and 
assessing, by means of a two dimensional vector (CV, FD), the relative role of both central 
and peripheral fatigue. 
 
Work supported by the European Project “Cybernetic Manufacturing Systems” (STREP n. 
016712) 


A new method to estimate myoelectric manifestation of muscle fatigue

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