Optimization of the Intelligent Sports Health Management System Based on Big Data Technology and Internet of Things

Abstract

People often suffer from unpredictable injuries during physical exercise. One of the important reasons is the absence of a scientific sports health management system. Therefore, the construction of such a scientific and effective system has gradually attracted the attention of scholars, which is of great significance to realizing people’s scientific and personalized physical fitness. An intelligent sports health management system based on big data analysis and the Internet of things (IoT) is constructed to solve this problem. The system consists of the user, IoT, cloud, system analysis, evaluation, and data layers. Firstly, a new multilabel feature selection algorithm is proposed in the system analysis layer. The suggested multilabel feature selection algorithm maps the sample space to the label space through the $L_{21}$ norm. Then, the consistency of various topologies is guaranteed by combining with feature popularity so that the factors affecting user health can be better selected. Secondly, the experiment is compared with SCLS, SSFS, and six other multilabel feature selection algorithms in 6 classic medical multilabel datasets. Experimental results under five indexes show the effectiveness and superiority of the proposed feature selection algorithm. Finally, the feasibility of the proposed intelligent sports management system is analyzed.