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AI in the Health of Sportspersons: Technologies, Applications, and Future Directions

  1. Introduction   Artificial Intelligence (AI) has emerged as a transformative force in modern sports, reshaping how athletes train, recover, prevent injuries, and optimize performance. Unlike traditional sports science methods that rely heavily on manual observations, subjective assessments, and periodic evaluations, AI introduces continuous monitoring, precision analytics, and predictive insights. These capabilities allow sportspersons, coaches, physiotherapists, and medical teams to make faster and more accurate decisions. AI’s integration into sports health is driven by advancements in machine learning, biomechanical modeling, wearable sensor technologies, computer vision, and neurophysiological signal processing. Together, these technologies ensure that athletes remain healthier, recover quicker, and maintain optimal performance throughout intense training cycles and competitive seasons. This article explores five popular AI applications in sportsperson health—injury pr...
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Neural Networks and Liquid Neural Networks: Concepts, Architecture, and Technical Foundations

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  Neural Networks and Liquid Neural Networks: Concepts, Architecture, and Technical Foundations      1. Introduction   Neural networks have become the foundation of modern artificial intelligence, powering applications ranging from vision and speech recognition to autonomous systems and large language models. While classical neural networks achieve high performance, their limitations in real-time adaptability, continuous learning, and dynamic decision-making have motivated the development of new architectures. One of the most promising advancements is the Liquid Neural Network (LNN), designed for adaptability, efficiency, and robustness in dynamic environments.   This document explains neural networks and liquid neural networks from a technical perspective, highlighting differences, mathematical intuition, and real-world applicability.      2. Basics of Artificial Neural Networks (ANNs)    2.1 Biological Inspiration   Artificia...
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