DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista brasileira de medicina do esporte (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922023000700203 |
Resumo: | ABSTRACT Introduction The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field. Objective Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students’ biological system. Methods According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes’ training load in the annual preparation period. The characteristics of athletes’ training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed. Results The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity. Conclusion The research shows that there can be two activities of “teaching” and “training” in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes. |
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DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEMDeep LearningPhysical Education and TrainingBiologyAthletic PerformanceABSTRACT Introduction The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field. Objective Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students’ biological system. Methods According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes’ training load in the annual preparation period. The characteristics of athletes’ training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed. Results The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity. Conclusion The research shows that there can be two activities of “teaching” and “training” in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.Sociedade Brasileira de Medicina do Exercício e do Esporte2023-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922023000700203Revista Brasileira de Medicina do Esporte v.29 n.spe1 2023reponame:Revista brasileira de medicina do esporte (Online)instname:Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)instacron:SBMEE10.1590/1517-8692202329012022_0197info:eu-repo/semantics/openAccessPeng,JianguiXu,Jianzhengeng2022-08-26T00:00:00Zoai:scielo:S1517-86922023000700203Revistahttp://www.scielo.br/rbmeONGhttps://old.scielo.br/oai/scielo-oai.php||revista@medicinadoesporte.org.br1806-99401517-8692opendoar:2022-08-26T00:00Revista brasileira de medicina do esporte (Online) - Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)false |
dc.title.none.fl_str_mv |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM |
title |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM |
spellingShingle |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM Peng,Jiangui Deep Learning Physical Education and Training Biology Athletic Performance |
title_short |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM |
title_full |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM |
title_fullStr |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM |
title_full_unstemmed |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM |
title_sort |
DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM |
author |
Peng,Jiangui |
author_facet |
Peng,Jiangui Xu,Jianzheng |
author_role |
author |
author2 |
Xu,Jianzheng |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Peng,Jiangui Xu,Jianzheng |
dc.subject.por.fl_str_mv |
Deep Learning Physical Education and Training Biology Athletic Performance |
topic |
Deep Learning Physical Education and Training Biology Athletic Performance |
description |
ABSTRACT Introduction The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field. Objective Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students’ biological system. Methods According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes’ training load in the annual preparation period. The characteristics of athletes’ training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed. Results The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity. Conclusion The research shows that there can be two activities of “teaching” and “training” in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922023000700203 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922023000700203 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1517-8692202329012022_0197 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Medicina do Exercício e do Esporte |
publisher.none.fl_str_mv |
Sociedade Brasileira de Medicina do Exercício e do Esporte |
dc.source.none.fl_str_mv |
Revista Brasileira de Medicina do Esporte v.29 n.spe1 2023 reponame:Revista brasileira de medicina do esporte (Online) instname:Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE) instacron:SBMEE |
instname_str |
Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE) |
instacron_str |
SBMEE |
institution |
SBMEE |
reponame_str |
Revista brasileira de medicina do esporte (Online) |
collection |
Revista brasileira de medicina do esporte (Online) |
repository.name.fl_str_mv |
Revista brasileira de medicina do esporte (Online) - Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE) |
repository.mail.fl_str_mv |
||revista@medicinadoesporte.org.br |
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1752122239918014464 |