Review Electromyographic models to assess muscle fatigue Miriam González-Izal a , Armando Malanda a , Esteban Gorostiaga b , Mikel Izquierdo c,⇑ a Dept. of Electrical and Electronic Engineering, Public University of Navarre, Spain b Studies, Research and Sport Medicine Center, Government of Navarre, Spain c Department of Health Sciences, Public University of Navarre, Spain article info Article history: Received 18 November 2011 Received in revised form 24 February 2012 Accepted 24 February 2012 Keywords: Fatigue EMG Models Neural network Multiple regression Training abstract Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables com- bined with the measurement of surface electromyography (sEMG) recordings (which can be measured during all types of movements) during exercise may be useful to assess and understand muscle fatigue. Therefore, there is a need to develop muscle fatigue models that relate changes in sEMG variables with muscle fatigue. However, the main issue when using conventional sEMG variables to quantify fatigue is their poor association with direct measures of fatigue. Therefore, using different techniques, several authors have combined sets of sEMG parameters to assess muscle fatigue. The aim of this paper is to serve as a state-of-the-art summary of different sEMG models used to assess muscle fatigue. This paper provides an overview of linear and non-linear sEMG models for estimating muscle fatigue, their ability to assess power loss and their limitations due to neuromuscular changes after a training period. Ó 2012 Elsevier Ltd. All rights reserved. Contents 1. Introduction ......................................................................................................... 502 2. The influence of muscle fatigue on surface electromyographic (sEMG) parameters ................................................ 502 2.1. sEMG amplitude-based parameters ................................................................................. 503 2.2. Spectral analysis ................................................................................................ 503 2.2.1. Mean and median frequency ............................................................................... 503 2.2.2. Dimitrov’s spectral fatigue index (FInsm5) .................................................................... 504 2.3. Time–frequency distributions ...................................................................................... 504 2.3.1. Instantaneous mean frequency (IMNF) ....................................................................... 504 2.3.2. Wavelet spectral parameters ............................................................................... 505 2.4. Non-linear parameters ........................................................................................... 505 2.4.1. Entropy ................................................................................................ 505 2.4.2. Fractal analysis .......................................................................................... 506 2.4.3. Recurrence quantification analysis (RQA) ..................................................................... 506 3. Relation between sEMG and power loss (measurement of muscle fatigue) ....................................................... 507 3.1. Linear techniques used to estimate muscle fatigue .................................................................... 508 3.2. Non-linear techniques used to estimate muscle fatigue ................................................................. 509 3.3. Linear vs. non-linear techniques used to estimate muscle fatigue......................................................... 509 3.4. Validation of the linear muscle fatigue mapping before and after training.................................................. 509 4. Conclusions and further studies ......................................................................................... 511 Acknowledgements ................................................................................................... 511 References .......................................................................................................... 511 1050-6411/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jelekin.2012.02.019 ⇑ Corresponding author. Address: Department of Health Sciences, Public University of Navarre, Avenida de Barañáin, s/n. 31008 Pamplona, Navarra, Spain. Tel.: +34 948 166140; fax: +34 948 270902. E-mail address: mikel.izquierdo@gmail.com (M. Izquierdo). Journal of Electromyography and Kinesiology 22 (2012) 501–512 Contents lists available at SciVerse ScienceDirect Journal of Electromyography and Kinesiology journal homepage: www.elsevier.com/locate/jelekin