Optimizing Swim Pose Estimation and Performance Analysis Using AI and Predictive Analytics

Abstract

Swim position estimation and performance analysis are critical for improving training outcomes and minimizing injury rates among swimmers of all skill levels. Traditional approaches rely heavily on manual video processing, which is timeconsuming and prone to human error, as well as biomechanical evaluations. Recent advances in artificial intelligence (AI) and predictive analytics have created new prospects for automating and improving these operations. The goal of this project is to create an artificial intelligence-powered swim position evaluation system that utilizes machine learning and computer vision techniques. The device monitors swimmers' activities in real time and gives exact feedback on technique, posture, and stroke efficiency. Predictive analytics improves this technique by predicting future trends and potential areas for development based on performance indicators and historical data. To provide precise, non-invasive human movement tracking in water, the proposed system employs biomechanical studies, deep learning models, and 3D pose estimation techniques