Prolonged and repeated fatigue conditions can cause muscle damage and adversely impact coordination in dynamic contractions. Hence it is important to determine the onset of muscle fatigue (OMF) in clinical rehabilitation and sports medicine. The aim of this study is to propose a method for analyzing surface electromyography (sEMG) signals and identify OMF using multifractal detrending moving average algorithm (MFDMA). Signals are recorded from biceps brachii muscles of twenty two healthy volunteers while performing standard curl exercise. The first instance of muscle discomfort during curl exercise is considered as experimental OMF. Signals are pre-processed and divided into 1-second epoch for MFDMA analysis. Degree of multifractality (DOM) feature is calculated from multifractal spectrum. Further, the variance of DOM is computed and OMF is calculated from instances of high peaks. The analysis is carried out by dividing the entire duration into six equal zones for time axis normalization. High peaks are observed in zones where subjects reported muscle discomfort. First muscle discomfort occurred in third and forth zones for majority of subjects. The calculated and experimental muscle discomfort zone closely matched in 72% of subjects indicating that multifractal technique may be a good method for detecting onset of fatigue. The experimental data may have an element of subjectivity in identifying muscle discomfort. This work can also be useful to analyze progressive changes in muscle dynamics in neuromuscular condition and co-contraction activity.