%A Agostini,Valentina %A Ghislieri,Marco %A Rosati,Samanta %A Balestra,Gabriella %A Knaflitz,Marco %D 2020 %J Frontiers in Neurology %C %F %G English %K Electromyography,EMG,Locomotion,machine learning,clinical practice,Rehabilitation,Physical Therapy,Outcome measurements %Q %R 10.3389/fneur.2020.00994 %W %L %M %P %7 %8 2020-September-04 %9 Hypothesis and Theory %# %! SEMG - Gait analysis %* %< %T Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics? %U https://www.frontiersin.org/articles/10.3389/fneur.2020.00994 %V 11 %0 JOURNAL ARTICLE %@ 1664-2295 %X Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements.