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对大数据中的健康相关事件检测进行分析,用于体育训练,运动检测和训练
Yi Cao1, Chongfei Li2, Cheng Yang2
1Eternal University of the Philippines No. 42, Hebei, Anhui, China.
概括
本研究介绍了用于数字体育训练分析的改进的等级K-means算法. 新模型使用大数据提高了健康事件检测的准确性,优于单一网络模型.
科学领域:
- 运动科学 运动科学 运动科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 中国的国家战略强调所有人的体育和健身.
- 数字记录和自动化训练系统对于体育发展至关重要.
- 运动捕捉技术使训练过程的数字记录和分析成为可能.
研究的目的:
- 为数字体育培训开发一个改进的等级K-means算法.
- 分析训练效应并使用准确的建模提供指导.
- 为了减少模型参数,提高体育训练分析中的识别速度.
主要方法:
- 将K-means与等级算法结合起来,创建一个新的等级K-means算法.
- 将算法应用于体育训练数据以进行聚类和分析.
- 将新算法的性能与现有方法进行比较.
主要成果:
- 提出的模型实现了平均准确率为91.27%和92.26%.
- 与单个网络模型相比,改进的算法显示出更高的性能.
- 证实了大数据对健康事件检测的有效利用.
结论:
- 改进的等级K-means算法在体育训练分析中提供了更高的准确性.
- 该模型有效地使用大数据检测健康事件.
- 这种方法支持将技术纳入国家健身战略.
相关概念视频
Overview of Biostatistics in Health Sciences
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
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Steps in Outbreak Investigation
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

