{"title":"Research on the Theory Model of Movement Commitment in the Movement of Different Time Structures in Sports Training","authors":"Yuting Wang","doi":"10.1109/ICVRIS.2019.00082","DOIUrl":null,"url":null,"abstract":"In order to monitor the effects of different levels of structural level exercise training, this paper proposes a method of different time structure level sports training in the Model of Motion Commitment Theory (MMCT). By using the statistical basis to measure the difference of motion in different time periods, in the process of monitoring the motion commitment theory model, the unmarked samples can be effectively marked by the motion commitment theory model, and the unlabeled samples are passed through the classifier. Confidence level is used to estimate the confidence level. The experimental results show that the proposed algorithm enhances the standardization, institutionalization and scientificization of sports training monitoring, and provides a scientific theoretical basis for the establishment of sports time science.","PeriodicalId":294342,"journal":{"name":"2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICVRIS.2019.00082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In order to monitor the effects of different levels of structural level exercise training, this paper proposes a method of different time structure level sports training in the Model of Motion Commitment Theory (MMCT). By using the statistical basis to measure the difference of motion in different time periods, in the process of monitoring the motion commitment theory model, the unmarked samples can be effectively marked by the motion commitment theory model, and the unlabeled samples are passed through the classifier. Confidence level is used to estimate the confidence level. The experimental results show that the proposed algorithm enhances the standardization, institutionalization and scientificization of sports training monitoring, and provides a scientific theoretical basis for the establishment of sports time science.