Data-Driven Injury-Risk Prediction in Competitive Swimming using Convolutional Neural Networks

Abstract

Recent advancements in computer vision and machine learning have improved real-time human motion analysis surpassing conventional biomechanical studies by focusing on sports accessibility and movement improvement. This paper presents SwimSight, a mobile application designed to identify injury-prone movements in swimming techniques and provide personalized prevention recommendations. The system consists of three primary stages, which are video processing and pose estimation, joint-angle analysis, and injury-risk prediction. The preprocessing stage enhances video quality through frame stabilization utilizing OpenCV based filters, conventional computer vision technique. Pose estimation is executed through a marker less pipeline built on modern 2D pose estimators, that extract stable 2D keypoints to convert them into joint angles. An ensemble of 1D-CNN and 2D-CNN deep learning models is applied to predict identify risky shoulder and knee (the most common injury areas) movements with improved generalization. The system further integrates movement clustering to tailor recommendations. Through this pipeline, SwimSight delivers a robust and user-friendly solution that advances performance by injury prevention in aquatic sports.