AI-Driven Gamification and Biotechnical Systems in Cardiovascular Monitoring of Cyclic Sport Athletes

¹ Candidate of technical sciences, docent, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent, Uzbekistan
² Candidate of biological sciences, professor, Uzbekistan State Physical Education and Sports University, Chirchiq city, Uzbekistan
³ Candidate of biological sciences, docent, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent, Uzbekistan

^* Corresponding author: saejrasukurova@gmail.com

Abstract

In this technologically advanced era, biotechnical systems and gamification have emerged as a possible prescription in the AI-driven cardiovascular monitoring of cyclic sport athletes. Scholars have noted that artificial intelligence is transforming the diagnostics, performance assessment, and the adaptive training, injury prevention, and real-time monitoring of athletes across the sports industry. The paper attempts to move forward research in biotechnical systems and gamification from traditional sports medicine, athlete conditioning, and exercise physiology contexts to emerging AI-enhanced frameworks that address current cardiovascular health challenges.In proposing such a novel approach, the authors reason why AHP-regression-based studies may be particularly suited for the iterative assessment, validation, and optimization of findings in the form of decision-making models such as personalized AI-driven training algorithms. Additionally, the analytical hierarchy process (AHP) framework is used to organize a systematic evaluation of predictive modeling techniques to identify some best practices related to specific biométrie parameters and athlete performance metrics.This methodological application then furthers the examination of the physiological and computational implications related to the use of AI-driven cardiovascular monitoring in terms of accuracy, efficiency, and adaptability. A closing case finally examines the role of a prominent gamification-based AI platform (i.e., Wearable AI-Assisted Training System) in the sports analytics-cardiovascular monitoring nexus.