Characterization of fatiguing muscle contractions is crucial for detection of neuromuscular diseases, in the area of sports medicine, ergonomics, and adequate designing of myoelectric control. Surface electromyography (sEMG) is a non-invasive technique that records the electrical activity of skeletal muscles. The extraction of reliable information from these signals is considered to be challenging as they possess multicomponent, nonlinear and non- stationary behaviour. Advanced time frequency analysis methods have been shown to provide significant information about the subtle variations in the signal. In this work, sEMG signals from Biceps Brachii are recorded and are analysed during fatiguing conditions. Fatigue-induced changes are explored using the Synchrosqueezed Wavelet Transform and singular value features. Automated analysis for the differentiation of nonfatigue and fatigue conditions is performed using different machine learning techniques. In this seminar, the objectives, methodology, significant results and contributions are presented.