Scholarlink

<p>The analysis of college student’ physical fitness data offers critical insights for enhancing health literacy and refining physical health strategies. Integrating principles of biomechanics, this study highlights the potential of privacy-preserving quantum computing methods to improve data analysis. Biomechanics, as the study of forces and their effects on the human body, provides a foundation for interpreting physical fitness data, including vital capacity, running performance, and flexibility scores. By leveraging a privacy-preserving quantum K-nearest neighbor algorithm, this research addresses the challenges of analyzing large-scale physical health datasets while maintaining data security. The proposed method ensures that encrypted training and test samples yield identical predictive outcomes as unencrypted data, thereby safeguarding sensitive attributes such as anthropometric and performance measures. Experiments using a dataset of 65,535 student records demonstrate the algorithm’s capability to classify accurately and cluster fitness attributes, including those relevant to biomechanical performance. Compared to existing quantum machine learning privacy schemes, this approach excels in terms of privacy protection, computational efficiency, and usability, offering a robust framework for biomechanical research and applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The changes in health biomechanics of college students based on quantum ML and big data analysis of physical fitness testing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

查看全文
  • Ganlin Liu

摘要

The analysis of college student’ physical fitness data offers critical insights for enhancing health literacy and refining physical health strategies. Integrating principles of biomechanics, this study highlights the potential of privacy-preserving quantum computing methods to improve data analysis. Biomechanics, as the study of forces and their effects on the human body, provides a foundation for interpreting physical fitness data, including vital capacity, running performance, and flexibility scores. By leveraging a privacy-preserving quantum K-nearest neighbor algorithm, this research addresses the challenges of analyzing large-scale physical health datasets while maintaining data security. The proposed method ensures that encrypted training and test samples yield identical predictive outcomes as unencrypted data, thereby safeguarding sensitive attributes such as anthropometric and performance measures. Experiments using a dataset of 65,535 student records demonstrate the algorithm’s capability to classify accurately and cluster fitness attributes, including those relevant to biomechanical performance. Compared to existing quantum machine learning privacy schemes, this approach excels in terms of privacy protection, computational efficiency, and usability, offering a robust framework for biomechanical research and applications.