Narrative review on AI digital twins for Taekwondo athlete data integration

BackgroundTaekwondo is a high-intensity Olympic combat sport that requires the integration of physical performance, tactical decision-making, and psychological resilience. Athletes face unique challenges such as rapid weight management, fatigue accumulation, injury risk, and competitive anxiety. While sports nutrition and psychological readiness are critical determinants of performance, they are often addressed separately, creating a gap in holistic, individualized athlete monitoring systems.MethodsThis narrative review synthesizes interdisciplinary evidence from sport science, nutrition, psychology, and artificial intelligence. A structured literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar, focusing on studies related to Taekwondo performance, weight-category nutrition strategies, psychological readiness, and AI-driven technologies including wearable systems, machine learning, and digital twin frameworks.ResultsThe findings indicate that AI-driven digital twin technology enables the integration of multidimensional athlete data, including nutritional intake, psychological state, training load, and physiological biomarkers (e.g., HRV and cortisol). These systems can generate actionable outputs such as readiness scoring, personalized nutrition strategies, early detection of fatigue and stress dysregulation, and prediction of injury or overtraining risk.ConclusionDigital twin technology represents a promising framework for transforming Taekwondo athlete management from fragmented monitoring to a holistic, data-driven approach. Practically, this may support coaches in making real-time decisions regarding training load, weight management, recovery, and psychological interventions. However, further empirical validation, ethical considerations, and applied research are required to support real-world implementation in elite combat sport environments.