Fatigue muscle detection using time-frequency methods

In order to detect muscle fatigue effectively, we recorded surface electromyographic (sEMG) signals on the right upper limbs of ten young men while they were implementing handgrip tasks. Wavelet packet transform and back propagation neural network were designed to extract features of sEMG and recognize the muscle states. 7-fold cross-validation was used to test the results. Our results showed a very efficient fatigue recognition using these methods even if a larger scale analysis would have been better. The study indicates that muscle fatigue could be detected by analyzing the sEMG signals, which allow us to consider a promising future for practical applications.