Intelligent recommendation method of an exercise program based on physical health data of college students

The development of society is greatly influenced by the physical strength of its individuals. As heirs of social construction, college students play a crucial role in national progress, and their physical health is an essential component of their well-being. However, the increasing competition for talent in today's rapidly advancing world has led to significant pressure on college students in various aspects of their lives. Despite the importance of physical exercise, students often lack the time and knowledge to engage in appropriate exercise programs that suit their individual needs. To address this issue, this paper proposes an improved K-means algorithm for the classification of college students' physical health data. The traditional K-means algorithm is known to be sensitive to noisy data, and thus, we introduce a variance-like weighting mechanism to improve its clustering accuracy. Our experimental results demonstrate that this algorithm can quickly and accurately cluster physical health data to provide a classification of each student's physical fitness. By using the physical classification of each student, we can recommend more suitable exercise programs to prioritize physical health management. This study highlights the significance of physical health in college students and encourages education departments to improve the efficiency of physical health management.

Automated summary

The physical health of college students is crucial for societal development, but they often lack suitable exercise programs due to competition and time constraints. This paper proposes an improved K-means algorithm to classify students' physical health data, recommending better exercise programs and enhancing the efficiency of physical health management.