International Journal on Science and Technology

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Integrated AI Technologies in Sports: A Technical Framework for Advanced Athletic Training Systems

Author(s) Sandeep Reddy Pakeer
Country United States
Abstract This article presents a comprehensive technical framework for implementing advanced artificial intelligence technologies in athletic training systems. The article integrates multiple AI components, including computer vision, deep learning, and reinforcement learning, to create a scalable and accessible
training platform. The article utilizes a multi-modal approach, combining real-time video analysis with IoT sensor data to provide comprehensive movement assessment and personalized coaching feedback.
The architecture implements edge computing and cloud-based processing to ensure optimal performance across various training environments. The article incorporates advanced security measures and privacy-preserving techniques while maintaining real-time feedback capabilities through mobile devices.
The article's modular design enables adaptation across different sports disciplines, utilizing sport-specific analytical modules and specialized AI components. This article addresses the significant gap between elite and grassroots level training opportunities by democratizing access to professional-grade training methodologies through innovative AI applications. The article details the technical implementation, validation methodologies, and future development roadmap for this integrated sports training system.
Keywords Artificial Intelligence in Sports; Athletic Performance Analysis; Multi-modal Sports Analytics; Edge Computing in Athletics; Intelligent Training Systems
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-21
Cite This Integrated AI Technologies in Sports: A Technical Framework for Advanced Athletic Training Systems - Sandeep Reddy Pakeer - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2595
DOI https://doi.org/10.71097/IJSAT.v16.i1.2595
Short DOI https://doi.org/g892gg
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