An Enhanced Sports Training Assistant System Based on Hybrid Artificial Intelligence Algorithm

In the era of intelligent sports, the integration of Artificial Intelligence (AI) into training processes has redefined athlete performance optimization. This paper presents the design and development of a Sports Training Assistant System based on advanced AI algorithms, aiming to deliver real-time feedback, performance analysis, and personalized training strategies. The system leverages a combination of Convolutional Neural Networks (CNNs) for motion detection and classification, along with Recurrent Neural Networks (RNNs) for temporal sequence analysis of athletic movements. The platform incorporates modules for pose estimation, biomechanical correction, fatigue prediction, and progress tracking, all powered by deep learning models trained on publicly available datasets such as Human3.6M, Kinetics, and OpenPose Sports Dataset. A custom dataset of annotated sports videos (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1,000+$</tex> samples across 5 sports categories) was also constructed to fine-tune model performance. The experimental results show that the system achieves an accuracy of 94.7 % in motion recognition, with a mean feedback latency of under 250 ms, enabling near real-time coaching interventions. Comparative evaluations against traditional coaching tools reveal significant improvements in training efficiency, injury prevention, and skill acquisition rates. The proposed system provides an intelligent, scalable, and sport-agnostic framework for augmenting coaching effectiveness, making it ideal for application in professional, academic, and fitness-based sports environments.