FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics

2026-07-17

AI Summary

This work addresses the fragmentation of current sports AI systems and the absence of a unified, explainable, and uncertainty-aware framework tailored for taekwondo. It proposes a trustworthy AI ecosystem integrating athlete intelligence, competition analytics, digital twins, and federation-level data governance. For the first time in taekwondo, the framework combines explainable AI with uncertainty quantification, leveraging multi-source data fusion, secure federated learning, and adaptive recommendation algorithms to deliver transparent and secure decision support. Prototype validation demonstrates its effectiveness in tactical diagnosis, longitudinal performance monitoring, outcome prediction, personalized training planning, and federation-wide benchmarking, highlighting its potential for broader applicability across competitive sports domains.

Abstract

The rapid digitalisation of elite sport has created new opportunities for integrating artificial intelligence (AI), performance analytics, and decision-support systems into athlete development and competition management. However, existing solutions remain fragmented, typically addressing isolated tasks such as performance analysis, athlete monitoring, or referee support. This paper presents \textbf{FST$\cdot$ai~2.5}, an explainable, uncertainty-aware, and secure AI framework for Olympic and Para-Taekwondo. \textbf{FST$\cdot$ai~2.5} introduces a unified digital ecosystem integrating athlete intelligence, competition analytics, federation-scale data management, AI-assisted decision support, athlete and event digital twins, explainable performance indicators, and adaptive training recommendations. The framework supports World Taekwondo (WT), Member National Associations (MNAs), coaches, referees, analysts, and athletes through transparent, secure, and federation-aware governance. By combining multi-source competition data, athlete-performance information, and contextual evidence, \textbf{FST$\cdot$ai~2.5} provides tactical diagnostics, longitudinal athlete monitoring, performance forecasting, personalised development planning, and federation-wide benchmarking using explainable and uncertainty-aware AI. Prototype deployments demonstrate the feasibility of the proposed framework. Although developed for Olympic and Para-Taekwondo, the methodology is broadly applicable to explainable AI, digital twins, and trustworthy decision support in combat sports and other high-performance sporting environments.

Problem

• explainable AI
• uncertainty-aware AI
• athlete digital twins
• federation-scale analytics
• decision support

Innovation

• Explainable AI
• Uncertainty-aware AI
• Digital Twins
• Federation-scale Analytics
• Decision Support System