High-density surface electromyography (HD-sEMG) offers unparalleled insights into muscle activity through high-resolution spatial and temporal data, yet its complexity hinders practical application. This study introduces a novel visual user interaction interface designed to simplify HD-sEMG data interpretation for athletic training. Leveraging advanced signal processing, the interface extracts and visualizes key metrics such as signal amplitude, frequency characteristics, and spatiotemporal activation patterns. Real-time monitoring, longitudinal analytics, and 3D musculoskeletal models provide actionable insights into training intensity, movement cycles, and fatigue indicators. Validated with data from 11 participants during bicep training, the user-centered interface ensures accessibility for diverse users, from clinicians to athletes. By transforming complex physiological data into intuitive visualizations, this work enhances the practical application of HD-sEMG, enabling optimized training, injury prevention, and rehabilitation strategies, and advancing the accessibility of this cutting-edge technology in both athletic and clinical settings.