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Sport Resource Classification Algorithm for Health Promotion Based on Cloud Computing: Rhythmic Gymnastics' Example.

Tairan Zhang1, Qing Han2, Zhenji Zhang1

  • 1School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.

Journal of Environmental and Public Health
|August 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a cloud-based algorithm to personalize rhythmic gymnastics teaching resources, enhancing health access. The method effectively classifies resources, improving data organization and individualization for better health promotion.

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Area of Science:

  • Sports Science
  • Computer Science
  • Health Informatics

Background:

  • Rhythmic gymnastics resource processing faces challenges like disorganized teaching materials and a lack of personalization.
  • Inefficient data management hinders health access to valuable teaching resources such as videos and documents.

Purpose of the Study:

  • To propose a cloud computing-based personalized rhythmic gymnastics teaching resource classification algorithm.
  • To enhance health access and promote personalized learning in rhythmic gymnastics.

Main Methods:

  • Designed a personalized rhythmic gymnastics teaching resource database utilizing cloud computing.
  • Preprocessed resources to create a meta-sample set and selected key characteristics.
  • Employed a vector space model for resource similarity calculation and distance-weighted k-NN for classification.

Main Results:

  • The proposed algorithm achieved high classification accuracy.
  • Demonstrated a high recall rate in resource classification.
  • Achieved a high F-measure value, confirming the algorithm's effectiveness.

Conclusions:

  • The cloud computing-based personalized classification algorithm is effective for rhythmic gymnastics resources.
  • The approach improves data organization and accessibility for health promotion.
  • Personalization of teaching resources enhances the learning experience in rhythmic gymnastics.