Surface Electromyography: What Limits Its Use in Exercise and Sport Physiology?
Open Access
- 6 November 2020
- journal article
- review article
- Published by Frontiers Media SA in Frontiers in Neurology
Abstract
The aim of the present paper is to examine to what extent the application of surface electromyography (sEMG) in the field of exercise and, more in general, of human movement, is adopted by professionals on a regular basis. For this purpose, a brief history of the recent developments of modern sEMG techniques will be assessed and evaluated for a potential use in exercise physiology and clinical biomechanics. The idea is to understand what are the limitations that impede the translation of sEMG to applied fields such as exercise physiology. A cost/benefits evaluation will be drawn in order to understand possible causes that prevents sEMG from being routinely adopted. Among the possible causative factors, educational, economic and technical issues will be considered. Possible corrective interventions will be proposed. We will also give an overview of the parameters that can be extracted from the decomposition of the sHDEMG signals and how this can be related by professionals for assessing the health and disease of the neuromuscular system. We discuss how the decomposition of surface EMG signals might be adopted as a new non-invasive tool for assessing the status of the neuromuscular system. Recent evidences show that is possible to monitor the changes in neuromuscular function after training of longitudinally tracked populations of motoneurons, predict the maximal rate of force development by an individual via motoneuron interfacing, and identify possible causal relations between aging and the decrease in motor performance. These technologies will guide our understanding of motor control and provide a new window for the investigation of the underlying physiological processes determining force control, which is essential for the sport and exercise physiologist. We will also illustrate the challenges related to extraction of neuromuscular parameters from global EMG analysis (i.e., root-mean-square, and other global EMG metrics) and when the decomposition is needed. We posit that the main limitation in the application of sEMG techniques to the applied field is associated to problems in education and teaching, and that most of the novel technologies are not open source.This publication has 34 references indexed in Scilit:
- Associations between motor unit action potential parameters and surface EMG featuresJournal of Applied Physiology, 2017
- Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separationJournal of Neural Engineering, 2016
- Characterization of Human Motor Units From Surface EMG DecompositionProceedings of the IEEE, 2016
- Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular functionJournal of Applied Physiology, 2015
- The extraction of neural strategies from the surface EMG: an update.Journal of Applied Physiology, 2014
- Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscleJournal of Neural Engineering, 2011
- Estimating motor unit discharge patterns from high-density surface electromyogramClinical Neurophysiology, 2009
- Experimental Simulation of Cat Electromyogram: Evidence for Algebraic Summation of Motor-Unit Action-Potential TrainsJournal of Neurophysiology, 2001
- Models of recruitment and rate coding organization in motor-unit poolsJournal of Neurophysiology, 1993
- Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal effortsJournal of Applied Physiology, 1981