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

BACKGROUND: Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics.

OBJECTIVE: This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision.

METHODS: The fitness and health of professional athletes are analyzed using information from a publicly available sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data.

RESULTS: The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos.

CONCLUSION: The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

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