The advent of high-density surface electromyography (HD-sEMG) has revolutionized muscle activity monitoring, offering unparalleled spatial and temporal resolution. However, the complexity and volume of HD-sEMG data pose significant analytical challenges, necessitating advanced computational and visualization tools. To address this, we developed an innovative visual user interaction system for muscle fatigue analysis, bridging electrophysiological data with practical applications. This system enhances usability for clinicians, sports scientists, and athletes, providing an intuitive platform for data interpretation to optimize muscle health, refine rehabilitation protocols, and improve athletic performance. Muscle fatigue, critical for performance optimization and injury prevention, requires precise quantification to guide evidence-based strategies. While HD-sEMG offers detailed insights into muscle dynamics, its complexity often hinders practical use. Our system integrates advanced visualization and user-friendly interaction techniques to transform complex data into actionable formats, enabling effective fatigue analysis. Unlike previous studies focusing on isometric contractions, our research explores dynamic contractions—more reflective of real-world activities—using Independent Component Analysis (ICA) to decompose HD-sEMG signals into motor unit (MU) activities. By analyzing MU synchronization across frequency bands during dynamic task in the biceps brachii, we observed increased synchronization during fatigue-inducing contractions. These findings highlight the importance of muscle-specific fatigue responses in designing training and rehabilitation programs, advocating for a holistic approach to performance and recovery. This research represents a significant leap in fatigue monitoring, with profound implications for clinical and athletic applications.