The precise determination of muscle activation timing is important in various fields such as motion analysis, biomechanical assessment in sports science, myoelectric control of prostheses and exoskeletons, diagnosis and follow-up of neuromuscular disorders through the evaluation of altered locomotion patterns and monitoring therapeutic interventions or rehabilitation programs. In spite of that, there is little consensus in literature on the methods for accurately detecting muscle activity onset/offset. The performance of traditional muscle activity detectors is significantly influenced by the signal-to-noise ratio (SNR) of surface electromyographic (sEMG) signals, the features used, and threshold settings. In recent years, machine learning-based methods have shown great potential in this task, encouraging further research in this direction.