Synthetic signals that represent fatiguing contractions of biceps brachii muscle are generated in this work using a comprehensive mathematical model. These signals are the biomarkers of muscle electrical activity that could be recorded non-invasively on the skin surface using Surface Electromyography (sEMG). The important components of the adopted synthetic sEMG model are current source, volume conductor, motor unit recruitment, and firing behavior functions. For this study, the amplitude (A) and scaling factor (λ) of the current source function is selected appropriate to fatiguing conditions. Further, tunable Q-wavelet method is applied to compute the frequency range associated with fatigue in the synthetic signal. The resultant wavelet coefficients are obtained using multirate filter bank where the scaling factors α and β are chosen so as to meet the anticipated Q-factor and the ranges of frequency bands. The results show that synthetically generated signal is able to truly represent fatiguing and nonfatiguing conditions. The amplitude-based features of tunable Q-wavelet coefficients are able to identify the characteristic changes associated with varied fatiguing conditions. Model generated frequency responses in fatiguing conditions are in agreement with the experimental results reported elsewhere. As fatigue is a temporary failure of skeletal muscles to maintain a required force for the accomplishment of a particular task, the model proposed here could be used as a validation of sEMG measurements in health and disease. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.