Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.
Exercises under isometric and dynamic contractions are influenced by the rate coding and recruitment strategies. The study of muscle strength under dynamic contraction is normally performed using one-repetition maximum (1-RM) method. There are several variants of deriving one repetition method using number of repetitions and load that are useful in physical fitness and clinical rehabilitation program. However, the factors of dynamic contractions such as endurance time, speed of muscle contractions and muscle activity are not considered in 1-RM methods. The muscular activities are analyzed using surface electromyography (sEMG) signals. Limited work has been reported on the relationship between the 1-RM method and factors such as endurance time, speed of contraction and sEMG activity. In this work, a modified 1-RM method is proposed, namely, N-RM, using load, number of repetitions, endurance time, speed of contraction and normalized sEMG activity. For this purpose, sEMG signals are recorded from 58 healthy subjects under standard dynamic contraction protocol involving curl exercise. Conventional 1-RM is computed by using Epley's method and compared with proposed method using correlation analysis. The results show that 1-RM increases linearly with number of curls (r=1) but has a poor correlation coefficient with sEMG (r=0.01) and endurance time (r =0.4). The curl speed for lower 1-RM and higher 1-RM did not show any statistical difference (p =0.2). The proposed N-RM is observed to have good correlation with endurance time (r=0.734), curl speed (r=0.893) and sEMG activity (r=0.8851). These results demonstrate that the proposed N-RM is highly correlated to factors influencing the dynamic contractions. This method can be further extended to assess muscles under various clinical disorders and sports training.
This study has described and experimentally validated the differential electrodes surface electromyography (sEMG) model for tibialis anterior muscles during isometric contraction. This model has investigated the effect of pennation angle on the simulated sEMG signal. The results show that there is no significant effect of pennation angle in the range 0° to 20° to the single fibre action potential shape recorded on the skin surface. However, the changes with respect to pennation angle are observed in sEMG amplitude, frequency and fractal dimension. It is also observed that at different levels of muscle contractions there is similarity in the relationships with Root Mean Square, Median Frequency, and Fractal Dimension of the recorded and simulated sEMG signals.
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