Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal

Muscle fatigue is defined as a reduction in the capability of muscle to exert force or power. Although surface electromyography (sEMG) signals during exercise have been used to assess muscle fatigue, analyzing the sEMG signal during dynamic contractions is difficult because of the many signal distorting factors such as electrode movements, and variations in muscle tissue conductivity. Besides the non-deterministic and non-stationary nature of sEMG in dynamic contractions, no fatigue indicator is available to predict the ability of a muscle to apply force based on the sEMG signal properties.

In this study, we designed and manufactured a novel wearable sensor system with both sEMG electrodes and motion tracking sensors to monitor the dynamic muscle movements of human subjects. We detected the state of muscle fatigue using a new wavelet analysis method to predict the maximum isometric force the subject can apply during dynamic contraction.

Our method of signal processing consists of four main steps. 1- Segmenting sEMG signals using motion tracking signals. 2- Determine the most suitable mother wavelet for discrete wavelet transformation (DWT) based on cross-correlation between wavelets and signals. 3- Deoinsing the sEMG using the DWT method. 4- Calculation of normalized energy in different decomposition levels to predict maximal voluntary isometric contraction force as an indicator of muscle fatigue.

The monitoring system was tested on healthy adults doing biceps curl exercises, and the results of the wavelet decomposition method were compared to well-known muscle fatigue indices in the literature.