基于运动健康大数据的大学生体质健康监测APP设计

IF 0.9 Q4 TELECOMMUNICATIONS
Xiaoni Zhang, Ran Li, Yunwei Li, Yunsheng Wang, Feilong Wu
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引用次数: 0

摘要

目前,大学生的生活习惯相对较差,运动量减少,导致体质越来越差。因此,人们开始研究大学生体质健康监测。机器学习和高性能计算在医疗领域的应用为智能医疗技术提供了技术支持。随着计算机网络的飞速发展,人类进入了信息化、数字化时代,运动健康大数据也越来越受到人们的青睐。体育健康大数据与学生体质健康监测技术的结合,从某种意义上讲,可以通过智能医疗、体育健康数据库等信息技术实现数据的自动处理,从而推动健康监测技术的普及。然而,目前的健康监测设备存在采集复杂、准确率低、健康数据处理受限等诸多问题。为解决这一问题,本文开发了一款基于运动健康大数据技术的应用程序(APP),可以监测人体心率、体温等多种生命体征,分析大学生的体质健康状况,使大学生在日常生活中可以方便地了解自己的健康状况,从而促进学生体质的健康发展,鼓励学生积极参加体育锻炼。实验证明,在心率监测中,当速度为 6 km/h 时,本文设计的大学生体质健康监测 APP 的误差率为 8.15%。学生步数监测的平均准确率为 97.12%。这表明该APP对人体生命体征监测功能的准确性和可用性。对于帮助大学生提高身体素质具有一定的应用价值和意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of College Students' physical health monitoring APP based on sports health big data

At present, the living habits of college students are relatively poor, and the amount of exercise is reduced, leading to their physical fitness getting worse and worse. Therefore, people began to study the physical health monitoring of college students. Machine learning and high-performance computing in medical applications provide technical support for intelligent medical technology. With the rapid development of computer network, human beings have entered the information and digital era, and sports health big data has become more and more popular. The combination of sports health big data and student physical health monitoring technology, in a sense, can realize automatic data processing through intelligent medical and sports health database and other information technologies, thus promoting the popularization of health monitoring technology. However, the current health monitoring equipment has many problems, such as complex collection, low accuracy and limited processing of health data. To solve this problem, this paper developed an Application (APP) based on sports health big data technology that can monitor multiple vital signs such as human heart rate and body temperature, and analyze the physical health of college students, so that college students can easily understand their health status in daily life, so as to promote the healthy development of students' physique and encourage them to actively participate in physical exercise. The experiment proved that, in the heart rate monitoring, when the speed is 6 km/h, the error rate of the college students' physical health monitoring APP designed in this paper is 8.15%. The average accuracy rate of student steps monitoring is 97.12%. This showed the accuracy and availability of the APP's monitoring function for human vital signs. It has certain application value and significance to help students improve their physical quality.

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