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In Frontiers in neurology

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.

Agostini Valentina, Ghislieri Marco, Rosati Samanta, Balestra Gabriella, Knaflitz Marco

2020

EMG, clinical practice, electromyography, locomotion, machine learning, outcome measurements, physical therapy, rehabilitation