基于云的高效体能训练脉搏监测与数据分析框架研究

Hongliang Yuan, Jun Wang, Jun Liu, Shiliang Li
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引用次数: 0

摘要

针对心率异常情况无法持续监测的问题,提出了一种基于云的脉搏监测与数据分析框架。该框架由多个基于ZigBee的脉冲监测传感器、定制网关和后端系统组成。个人脉搏信息通过传感器采集,传递给后端云系统,支持培训条件的大数据分析。为了保证采集到有效的脉搏信号,我们研究了基于光电的动态连续心率监测方法和综合抗干扰方法。最后运用大数据分析方法,建立了以不同年龄、不同情绪等为标准的训练模型。结果表明,该系统可以提高运动员的体能训练水平,积累个人的训练数据,支持更高效、科学的训练计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of Clouding Based Pulse Monitoring and Data Analysis Framework for Efficient Physical Training
For the problems that we can't monitor abnormal conditions of heart rate continuously, a clouding based pulse monitoring and data analysis framework has been proposed. Source of the framework is composed of multiple ZigBee based pulse monitoring sensors, customized gateways and back-end system. Individuals' pulse information are collected by sensors and passed to back-end clouding system to support big data analysis of the training conditions. To guarantee collecting efficient pulse signal, we have researched photo electricity based dynamic and continuous heart rate monitoring methods as well as comprehensive anti-jamming methods. Finally, by using according big data analysis methods we have built up the training model by the standards such as different age, different mood and so on. Results shows the system can be used to improve the physical training level, accumulate the training data of the individuals and support more efficient and scientific training plans.
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