基于微信小程序的运动数据分析与可视化研究  

Motion Data Analysis and Visualization Research Based on WeChat Mini Programs

作　　者：李行 LI Hang(College of Information Engineering,Xizang Minzu University,Xianyang 712082,China)

机构地区：[1]西藏民族大学信息工程学院,陕西咸阳712082

出　　处：《现代信息科技》2023年第18期55-59,共5页Modern Information Technology

基　　金：2022年国家级大学生创新创业训练计划(202210695025)。

摘　　要：运动数据可视化技术在运动人员的运动表现和伤病预测管理方面的应用越来越受关注,文章对运动数据进行了分析和可视化研究。运动数据可视化通过记录运动人员的运动表现,提高人们运动的满足感,同时有利于实现对运动人员运动中损伤的预测管理。以微信小程序为载体,通过收集和分析用户数据,实现基于ec-canvas组件的运动数据折线图和柱状图的绘制,为用户提供更为直观和个性化的运动数据展示和管理服务。在运动人员运动表现可视化研究中,采用K-means聚类算法对运动人员的心率和运动时长数据进行分析,通过散点图展示聚类结果。研究表明,运动数据的可视化分析可以帮助运动人员评估自身状态和运动表现。The application of motion data visualization technology in the management of sports performance and injury prediction for athletes is receiving increasing attention.This paper analyzes and visualizes motion data.Visualization of motion data improves people's satisfaction with exercise by recording their performance,while also facilitating the prediction and management of injuries during exercise.Using WeChat mini programs as the carrier,by collecting and analyzing user data,line and bar charts of motion data based on the ec-canvas component can be drew,providing users with more intuitive and personalized motion data display and management services.In the study of visualizing the performance of athletes,the K-means clustering algorithm is used to analyze the heart rate and exercise duration data of athletes,and the clustering results are displayed through scatter plots.Research has shown that visual analysis of motion data can help athletes evaluate their own state and sports performance.

关 键 词：运动数据 可视化 微信小程序 伤病预测管理 算法模型 风险指标 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]