Surface electromyography (EMG) is a valuable tool in clinical diagnostics and research related to human neuromotor control. Non-linear analysis of EMG data can help with detection of subtle changes of control due to changes of external or internal constraints during motor tasks. However, non-linear analysis is complex and results may be difficult to interpret, particularly in clinical environments. We developed a non-linear analysis tool (SYNERGOS) that evaluates multiple muscle activation (MMA) features and provides a single value for description of activation characteristics. To investigate the responsiveness of SYNERGOS to kinetic changes during cyclic movements, 13 healthy young adults performed squat movements under different loading conditions (100%–120% of body weight). We processed EMG data to generate SYNERGOS indices and used two-way repeated measures ANOVA to determine changes of MMA in response to loading conditions during movement. SYNERGOS values were significantly different for each loading condition. We concluded that the algorithm is sensitive to kinetic changes during cyclic movements, which may have implications for applications in a variety of experimental and diagnostic settings.