Big data analytics for image processing and computer vision technologies in sports health management.

This article focuses on the development and evaluation of a Big Data Analytic-assisted Computer Vision Model (BD-CVM) designed to improve the management, classification, and visualization of dynamic health data for professional athletes. Utilizing machine learning techniques, particularly recurrent neural networks (RNN), and big data analytics, the BD-CVM processes multimodal sports data—including video and sensor inputs—to enhance accuracy, precision, and prediction of athlete performance and health status. The model incorporates an error analysis module to reduce classification errors and supports real-time monitoring through wearable sensors, enabling better injury prediction and personalized health management. Experimental results demonstrate that BD-CVM outperforms existing methods in athlete performance ratio, health outcome ratio, prediction accuracy, and efficiency, while acknowledging challenges related to data quality, ethical considerations, and algorithm generalizability in sports health analytics.