Mechanomyography versus Electromyography, in monitoring the muscular fatigue

BACKGROUND: The use of the mechanomyogram (MMG) which detects muscular vibrations generated by fused individual fiber twitches has been refined. The study addresses a comparison of the MMG and surface electromyogram (SEMG) in monitoring muscle fatigue. METHODS: The SEMG and MMG were recorded simultaneously from the same territory of motor units in two muscles (Biceps, Brachioradialis) of the human (n = 18), during sustained contraction at 25 % MVC (maximal voluntary contraction). RESULTS: The RMS (root mean square) of the SEMG and MMG increased with advancing fatigue; MF (median frequency) of the PSD (power density spectra) progressively decreased from the onset of the contraction. These findings (both muscles, all subjects), demonstrate both through the SEMG and MMG a central component of the fatigue. The MF regression slopes of MMG were closer to each other between men and women (Biceps 1.55%; Brachialis 13.2%) than were the SEMG MF slopes (Biceps 25.32%; Brachialis 17.72%), which shows a smaller inter-sex variability for the MMG vs. SEMG. CONCLUSION: The study presents another quantitative comparison (MF, RMS) of MMG and SEMG, showing that MMG signal can be used for indication of the degree of muscle activation and for monitoring the muscle fatigue when the application of SEMG is not feasible (chronical implants, adverse environments contaminated by electrical noise)

Optimal elbow angle for MMG signal classification of biceps brachii during dynamic fatiguing contraction

Mechanomyography (MMG) activity of the biceps muscle was recorded from thirteen subjects. Data was recorded while subjects performed dynamic contraction until fatigue. The signals were segmented into two parts (Non-Fatigue and Fatigue), An evolutionary algorithm was used to determine the elbow angles that best separate (using DBi) both Non-Fatigue and Fatigue segments of the MMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted MMG trials. After completing twenty-six independent evolution runs, the best run containing the best elbow angles for separation (fatigue and non-fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on eight features that where extracted from each of the two classes (non-fatigue and fatigue) to quantify the performance. Results show that the elbow angles produced by the Genetic algorithm can be used for classification showing 80.64% highest correct classification for one of the features and on average of all eight features including worst performing features giving 66.50%