2021
DOI: 10.1155/2021/9608147
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Health Analysis of Footballer Using Big Data and Deep Learning

Abstract: With the development of information technology, health management and big data have risen and developed in recent years. Big data need proper analysis and shape in order to extract meaningful information from it. This paper analyzes the application status and prospects of big data in the field of health management. The results show that the most widely used big data in health management are intelligent wearable devices. Big data applications in football players’ mental health monitoring systems and chronic dis… Show more

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Cited by 11 publications
(6 citation statements)
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“…The four data sources are pooled into the database to focus on evaluating the teaching situation of teachers and students, conduct correlation evaluation according to the focus of the learning courseware and the learning performance, and finally recommend the learning courseware and professional teachers with higher matching degree for students, and recommend the test questions according to their learning level and learning direction. [7][8][9][10] The core of the whole system is to build a matching model according to teachers and students, so as to provide professional English teachers with higher matching degree for students' learning. The overall model contains the following judgment criteria: First, obtain the relevant knowledge points and scores of each test question from the examination score records participated by students, and then analyze the students' learning time in this knowledge point and the number of courseware, and then upload it to professional teachers; Secondly, if a student spends less time studying a certain knowledge point than other knowledge points and gets a lower score than other knowledge points, the number of recommended questions for this kind of knowledge should be increased.…”
Section: Methods 21 Requirement Analysismentioning
confidence: 99%
“…The four data sources are pooled into the database to focus on evaluating the teaching situation of teachers and students, conduct correlation evaluation according to the focus of the learning courseware and the learning performance, and finally recommend the learning courseware and professional teachers with higher matching degree for students, and recommend the test questions according to their learning level and learning direction. [7][8][9][10] The core of the whole system is to build a matching model according to teachers and students, so as to provide professional English teachers with higher matching degree for students' learning. The overall model contains the following judgment criteria: First, obtain the relevant knowledge points and scores of each test question from the examination score records participated by students, and then analyze the students' learning time in this knowledge point and the number of courseware, and then upload it to professional teachers; Secondly, if a student spends less time studying a certain knowledge point than other knowledge points and gets a lower score than other knowledge points, the number of recommended questions for this kind of knowledge should be increased.…”
Section: Methods 21 Requirement Analysismentioning
confidence: 99%
“…In the process of building an intelligent evaluation model, a large number of data related to physical education are collected, pretreated and cleaned. Then, the deep learning technology is used to learn and represent the features of the data, and the feature vector of each student is obtained [28] . Then, the feature vector is further analyzed and processed by DM technology, and the input data needed for evaluating the model is obtained.…”
Section: B Big Data Driven Assessment Algorithm For Pementioning
confidence: 99%
“…In the second step, a more rigorous clustering algorithm is selected to perform clustering operations on the data in Canopy obtained in the first step. e Canopy algorithm is a clustering strategy of thick and thin sets, and it is very suitable for preanalysis of high-dimensional data [12,13]. e Canopy clustering process is shown in Figure 2.…”
Section: Canopy Algorithmmentioning
confidence: 99%