Fuzzy based analysis method of high-density surface electromyography maps for physical training assessment

As sport becomes more competitive, the importance of physical training assessment increases. This paper proposes a new method based on fuzzy analysis techniques for comparing images of muscle activity attained by employing high-density surface electromyography (sEMG). Along with other evaluation techniques, the proposed method could be useful to monitor the evolution of sportsmen's physical training. High-density sEMG maps were generated from sEMG signals acquired from the vastus medialis muscle of the right leg of healthy subjects. The fuzzy logic assessment method has two input variables: the muscle activation area and the mean intensity of muscle activity. Results are displayed on a graphical interface and the method was implemented as a modular, distributed application. The obtained results highlight important differences among sEMG images without pointing out the underlying physiological mechanisms. Future investigations regarding the applicability of the proposed method for long training cycles could provide crucial information about the changes in muscle activity that occur with physical exercise.