Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks.

The electromyography (EMG) signals give information about different features of muscle function. Real-time measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using time-frequency methods and independent component analysis (ICA). In order to realize this aim, EMG activity obtained from activated muscle during a phasic voluntary movement was recorded for 14 healthy young persons and EMG signals were observed in time-frequency domain for determination of fatigue. Time-frequency methods are used for the processing of signals that are non-stationary and time varying. The EMG contains transient signals related to muscle activity. The proposed method for the detection of muscle fatigue is automated by using artificial neural networks (ANN). The results show that ANN with ICA separates EMG signals from fresh and fatigued muscles, hence providing a visualization of the onset of fatigue over time. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.