Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

Background And Objective: Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals.

A computational model to investigate the effect of pennation angle on surface electromyogram of Tibialis Anterior.

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.

Analysis of one repetition during biceps curl exercise among age-matched adult volunteers using endurance, curl speed and surface electromyography signals.

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.