Multimodal sensing and decision-making for evaluating the physical fitness of university students using body area network.

These days, physical health concerns are gradually elevated to the same degree of significance as essential issues such as education, health, and protection as national living standards improve. Enhancing the physical health of university students and encouraging healthy growth are significant moments for China, due to which each university controls a physical examination every year. Utilizing wireless sensors for data capture in university students' physical fitness evaluation systems has played an essential role in various areas. Therefore, this paper presents a body domain network multimodal sensor and decision algorithm-based physical health assessment system for university students. The proposed system uses multiple body domain network sensors to collect physiological indicators data by using decision algorithms to combine multiple assessment indicators for analysis and prediction, ultimately generating a health assessment report and providing customized health advice and health plans for each user. In addition, to improve the physical health evaluation of university students in the context of sports medicine integration, this paper also proposes a physical health evaluation method based on the ID3 decision tree algorithm. It constructs a physical health information mining and feature extraction model for university students in the context of sports medicine integration. It uses a decision tree optimization method for classification detection. The proposed method also uses an associative multidimensional feature detection algorithm to evaluate university students' physical fitness and health status. It establishes a decision indicator function to investigate information fusion and constrained feature decomposition through the significant difference and balanced training fusion methods. In the ID3 decision tree, the branching system of college students' physical fitness health information in the context of sports medicine fusion is designed. It examines the entropy weight index parameters for managing test data management to assess university students' physical fitness in fusing sports medicine. The experimental results show that the system can accurately assess the physical health status of university students and provide corresponding recommendations and plans, which is essential for improving the health status of university students. In addition, these results proved that the proposed method has good output stability and strong optimization ability, which improves the classification management and information integration of college students' physical fitness and health information. [ABSTRACT FROM AUTHOR]