Design of a Mobile Big Data Processing-Based Sports Health Evaluation System Using Graph Neural Network

Abstract

The sports health evaluation has been a technical concern in modern society, and massive mobile big data can be employed as the resource support for this purpose. In this context, this paper presents design of a mobile big data processing-based sports health evaluation system, with adopting the graph neural network model as the algorithm part. First of all, a Zigbee-based mobile big data integration platform is designed for sports health demand. Then, a graph neural network model is developed and embedded into such platform to suggest evaluation results according to collected sprots big data. Inside the whole designed system, the collaborative workflow between business support part and algorithmic analysis part is defined accordingly. Finally, some simulation conditions are set to make assessment for the designed technical framework with introduction of real-world sports big data. The obtained simulation results demonstrate the effectiveness and reliability of the designed evaluation system. The proposal can possess both proper big data processing efficiency and proper system operation performance. Under the designed big data computing framework, a sports big data processing platform has been implemented to meet the technical requirements of upper level applications for data storage, management, and analysis. It provides theoretical support for the development and progress of sports.

Keywords

• Sports healthcare
• deep learning
• big data analytics
• mobile computing