Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61317 |
Resumo: | Low levels of physical activity (PA) are related to an increased risk of death, hypertension, coronary disease, stroke, diabetes, and depression. Then, assessing the level of PA of a person is essential to create training programs that help prevent such risks. However, current measurements of PA are mainly subjective and tend to underestimate or overestimate the PA level of a person. This article intends the result of a pilot cross-sectional feasibility study that pretends to classify the PA level through direct and objective measurements. For this, direct measurements such as anthropometric and postural sway (PS) features from fifteen participants (8 Male and 7 Women) were obtained. To predict the level of PA machine learning technique of Support Vector Machines SVM was used. The classifier showed high F1, recall, and precision scores around 80%, and after feature importance selection and hyperparameter were tunned, they reached 100%. Results suggest that the use of direct measurements to classify the PA level is feasible and that there is a correlation between direct measurements and the IPAQ-SF, an indirect measurement that is typically used to assess the level of PA. This classifier intends to be a tool that helps trainers and physicians to endorse or adjust their physical training and rehabilitation procedures based on the objective evaluation of patients. |
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Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study.machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study.Low levels of physical activity (PA) are related to an increased risk of death, hypertension, coronary disease, stroke, diabetes, and depression. Then, assessing the level of PA of a person is essential to create training programs that help prevent such risks. However, current measurements of PA are mainly subjective and tend to underestimate or overestimate the PA level of a person. This article intends the result of a pilot cross-sectional feasibility study that pretends to classify the PA level through direct and objective measurements. For this, direct measurements such as anthropometric and postural sway (PS) features from fifteen participants (8 Male and 7 Women) were obtained. To predict the level of PA machine learning technique of Support Vector Machines SVM was used. The classifier showed high F1, recall, and precision scores around 80%, and after feature importance selection and hyperparameter were tunned, they reached 100%. Results suggest that the use of direct measurements to classify the PA level is feasible and that there is a correlation between direct measurements and the IPAQ-SF, an indirect measurement that is typically used to assess the level of PA. This classifier intends to be a tool that helps trainers and physicians to endorse or adjust their physical training and rehabilitation procedures based on the objective evaluation of patients.Low levels of physical activity (PA) are related to an increased risk of death, hypertension, coronary disease, stroke, diabetes, and depression. Then, assessing the level of PA of a person is essential to create training programs that help prevent such risks. However, current measurements of PA are mainly subjective and tend to underestimate or overestimate the PA level of a person. This article intends the result of a pilot cross-sectional feasibility study that pretends to classify the PA level through direct and objective measurements. For this, direct measurements such as anthropometric and postural sway (PS) features from fifteen participants (8 Male and 7 Women) were obtained. To predict the level of PA machine learning technique of Support Vector Machines SVM was used. The classifier showed high F1, recall, and precision scores around 80%, and after feature importance selection and hyperparameter were tunned, they reached 100%. Results suggest that the use of direct measurements to classify the PA level is feasible and that there is a correlation between direct measurements and the IPAQ-SF, an indirect measurement that is typically used to assess the level of PA. This classifier intends to be a tool that helps trainers and physicians to endorse or adjust their physical training and rehabilitation procedures based on the objective evaluation of patients.Universidade Estadual De Maringá2023-04-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/6131710.4025/actascitechnol.v45i1.61317Acta Scientiarum. Technology; Vol 45 (2023): Publicação contínua; e61317Acta Scientiarum. Technology; v. 45 (2023): Publicação contínua; e613171806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61317/751375155828Copyright (c) 2023 Acta Scientiarum. Technologyhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessRivera, OswaldoAvilés, Oscar Fernando Castillo-Castaneda, Eduardo 2023-05-25T13:57:06Zoai:periodicos.uem.br/ojs:article/61317Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2023-05-25T13:57:06Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study |
title |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study |
spellingShingle |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study Rivera, Oswaldo machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study. machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study. |
title_short |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study |
title_full |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study |
title_fullStr |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study |
title_full_unstemmed |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study |
title_sort |
Classifying the physical activity indicator using machine learning and direct measurements: a feasibility study |
author |
Rivera, Oswaldo |
author_facet |
Rivera, Oswaldo Avilés, Oscar Fernando Castillo-Castaneda, Eduardo |
author_role |
author |
author2 |
Avilés, Oscar Fernando Castillo-Castaneda, Eduardo |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Rivera, Oswaldo Avilés, Oscar Fernando Castillo-Castaneda, Eduardo |
dc.subject.por.fl_str_mv |
machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study. machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study. |
topic |
machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study. machine learning; postural Sway; physical activity; SVM; IPAQ; feasibility study. |
description |
Low levels of physical activity (PA) are related to an increased risk of death, hypertension, coronary disease, stroke, diabetes, and depression. Then, assessing the level of PA of a person is essential to create training programs that help prevent such risks. However, current measurements of PA are mainly subjective and tend to underestimate or overestimate the PA level of a person. This article intends the result of a pilot cross-sectional feasibility study that pretends to classify the PA level through direct and objective measurements. For this, direct measurements such as anthropometric and postural sway (PS) features from fifteen participants (8 Male and 7 Women) were obtained. To predict the level of PA machine learning technique of Support Vector Machines SVM was used. The classifier showed high F1, recall, and precision scores around 80%, and after feature importance selection and hyperparameter were tunned, they reached 100%. Results suggest that the use of direct measurements to classify the PA level is feasible and that there is a correlation between direct measurements and the IPAQ-SF, an indirect measurement that is typically used to assess the level of PA. This classifier intends to be a tool that helps trainers and physicians to endorse or adjust their physical training and rehabilitation procedures based on the objective evaluation of patients. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04-28 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61317 10.4025/actascitechnol.v45i1.61317 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61317 |
identifier_str_mv |
10.4025/actascitechnol.v45i1.61317 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61317/751375155828 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 45 (2023): Publicação contínua; e61317 Acta Scientiarum. Technology; v. 45 (2023): Publicação contínua; e61317 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315338097065984 |