Health care data analysis and visualization using interactive data exploration for sportsperson  

作　　者：Hao LIU Yang ZHANG Ke LIAN Yifei ZHANG Oscar Sanjuán MARTÍNEZ Rubén González CRESPO 

机构地区：[1]Department of Sport and Exercise Sciences,Kunsan National University,Jeobuk 54150,Republic of Korea [2]Department of Naval Architecture and Ocean Engineering,Ludong University,Yantai 264025,China [3]Computer Science Department,School of Engineering and Technology,Universidad Internacionalde La Rioja,Madrid 26006,Spain

出　　处：《Science China(Information Sciences)》2022年第6期81-93,共13页中国科学（信息科学）（英文版）

摘　　要：Sports have scored significant attention among the public in this multifaceted world.Diverse training strategies are followed by many athletics and even flexible to adapt comfortable and optimal techniques.This fact has led physicians and educators to encourage remote health surveillance as one of the core strategies in athletic training.The need for innovative data exploration methodologies capable of facing Big Data’s influence to make remote monitoring services viable has been raised by the growing ties of networks that deliver high quantities of real-time data.This paper presents an interactive healthcare data exploration and visualization(IHDEV)model to enhance multi-scaling data analysis and visualization in the athletic health vision platform.This paper aims to simplify optimization methods to measure sportsperson muscle tension.This model illustrates a three-layer architecture with a raw data acquisition layer,data analysis layer,and visualization layer.The first layer considers the acquisition of health-related data from the athletes for remote monitoring using IoT and stores it into the cloud.The data analysis layer adapts artificial intelligence(AI)in data mining.The final layer introduces an intelligent interactive data visualization model assisted by a reactive workflow mechanism,enabling analysis and visualization solutions to be composed in a personalized data flow appropriate to the athletic training.This experimental study extended with two healthcare datasets to show the feasibility of IHDEV in promoting healthcare based athletic monitoring and improves the accuracy ratio of 96.7%,prediction ratio of 96.2%,an efficiency ratio of 96.8%,Pearson correlation coefficient of 98.2%,and reduces the error rate of 18.7% compared to other conventional models.

关 键 词：artificial intelligence health monitoring data visualization data analysis ATHLETICS 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] G808.18[自动化与计算机技术—计算机科学与技术]