A method for quantitative SEMG decomposition and MUAP classification during voluntary isovelocity elbow flexion

The purpose of this study was to develop an algorithm for surface electromyogram (SEMG) decomposition and classification of surface motor unit (MU) action potential (MUAP) detected during isovelocity elbow flexion. In our proposed algorithm, firstly the measured SEMG was extracted for 3 seconds by every 1.5 seconds. SEMG was decomposed with Independent Component Analysis (ICA) technique, and classified with template matching. Finally, the MUAP trains were identified under the firing time of the MUAPs classified in each extracted period. The SEMG was measured from the biceps short head muscle during voluntary elbow flexion of 0 to 90 degrees at constant velocity 9 degree/s against a constant load torque of 10%MVC and the MUAPs were classified with our proposed algorithm. As a result, calculated MUs firing rates were almost same as the results in the previous studies. It was shown that the proposed algorithm was useful for decomposing SEMG detected during flexion movements.