Upper Limb Motion Tracking and Classification: A Smartphone Approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1145/3470482.3479618 http://hdl.handle.net/11449/222587 |
Resumo: | Due to the evolution of motion capture devices, natural user interfaces have been applied in several areas, such as neuromotor rehabilitation supported by virtual environments. This paper presents a smartphone application that allows the user to interact with the virtual environment and enables the captured data to be stored, processed, and used in machine learning models. The application submits the recordings to the remote database with information about the movement and in order to apply supervised machine learning. As a proof of concept, we generated a dataset capturing movement data using our application with 232 instances divided into 8 classes of movements. Moreover, we used this dataset for training models that classifies these movements. The remarkable accuracy of the models shows the feasibility of using body articulation data for a classification task after some data transformations. |
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Repositório Institucional da UNESP |
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Upper Limb Motion Tracking and Classification: A Smartphone Approachaugmented realityComputer visionmotion capturesupervised machine learningDue to the evolution of motion capture devices, natural user interfaces have been applied in several areas, such as neuromotor rehabilitation supported by virtual environments. This paper presents a smartphone application that allows the user to interact with the virtual environment and enables the captured data to be stored, processed, and used in machine learning models. The application submits the recordings to the remote database with information about the movement and in order to apply supervised machine learning. As a proof of concept, we generated a dataset capturing movement data using our application with 232 instances divided into 8 classes of movements. Moreover, we used this dataset for training models that classifies these movements. The remarkable accuracy of the models shows the feasibility of using body articulation data for a classification task after some data transformations.São Paulo State University-UNESPFederal University of São João Del-Rei-UFSJ Brazil and Brazilian Institute of Neuroscience and Neurotechnology-BRAINNBrazilian Institute of Neuroscience and Neurotechnology-BRAINNFederal University of São João Del-Rei-UFSJSão Paulo State University-UNESP Brazil and Brazilian Institute of Neuroscience and Neurotechnology-BRAINNSão Paulo State University-UNESPSão Paulo State University-UNESP Brazil and Brazilian Institute of Neuroscience and Neurotechnology-BRAINNUniversidade Estadual Paulista (UNESP)Universidade Federal de Sergipe (UFS)Brazilian Institute of Neuroscience and Neurotechnology-BRAINNRodrigues, Luis. G. S. [UNESP]Dias, Diego R. C.Guimarães, Marcelo P.Brandão, Alexandre F.Rocha, Leonardo C. D. [UNESP]Iope, Rogério L. [UNESP]Brega, José R. F. [UNESP]2022-04-28T19:45:31Z2022-04-28T19:45:31Z2021-11-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject61-64http://dx.doi.org/10.1145/3470482.3479618ACM International Conference Proceeding Series, p. 61-64.http://hdl.handle.net/11449/22258710.1145/3470482.34796182-s2.0-85116586541Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengACM International Conference Proceeding Seriesinfo:eu-repo/semantics/openAccess2022-04-28T19:45:31Zoai:repositorio.unesp.br:11449/222587Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:39:15.039061Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Upper Limb Motion Tracking and Classification: A Smartphone Approach |
title |
Upper Limb Motion Tracking and Classification: A Smartphone Approach |
spellingShingle |
Upper Limb Motion Tracking and Classification: A Smartphone Approach Rodrigues, Luis. G. S. [UNESP] augmented reality Computer vision motion capture supervised machine learning |
title_short |
Upper Limb Motion Tracking and Classification: A Smartphone Approach |
title_full |
Upper Limb Motion Tracking and Classification: A Smartphone Approach |
title_fullStr |
Upper Limb Motion Tracking and Classification: A Smartphone Approach |
title_full_unstemmed |
Upper Limb Motion Tracking and Classification: A Smartphone Approach |
title_sort |
Upper Limb Motion Tracking and Classification: A Smartphone Approach |
author |
Rodrigues, Luis. G. S. [UNESP] |
author_facet |
Rodrigues, Luis. G. S. [UNESP] Dias, Diego R. C. Guimarães, Marcelo P. Brandão, Alexandre F. Rocha, Leonardo C. D. [UNESP] Iope, Rogério L. [UNESP] Brega, José R. F. [UNESP] |
author_role |
author |
author2 |
Dias, Diego R. C. Guimarães, Marcelo P. Brandão, Alexandre F. Rocha, Leonardo C. D. [UNESP] Iope, Rogério L. [UNESP] Brega, José R. F. [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade Federal de Sergipe (UFS) Brazilian Institute of Neuroscience and Neurotechnology-BRAINN |
dc.contributor.author.fl_str_mv |
Rodrigues, Luis. G. S. [UNESP] Dias, Diego R. C. Guimarães, Marcelo P. Brandão, Alexandre F. Rocha, Leonardo C. D. [UNESP] Iope, Rogério L. [UNESP] Brega, José R. F. [UNESP] |
dc.subject.por.fl_str_mv |
augmented reality Computer vision motion capture supervised machine learning |
topic |
augmented reality Computer vision motion capture supervised machine learning |
description |
Due to the evolution of motion capture devices, natural user interfaces have been applied in several areas, such as neuromotor rehabilitation supported by virtual environments. This paper presents a smartphone application that allows the user to interact with the virtual environment and enables the captured data to be stored, processed, and used in machine learning models. The application submits the recordings to the remote database with information about the movement and in order to apply supervised machine learning. As a proof of concept, we generated a dataset capturing movement data using our application with 232 instances divided into 8 classes of movements. Moreover, we used this dataset for training models that classifies these movements. The remarkable accuracy of the models shows the feasibility of using body articulation data for a classification task after some data transformations. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-05 2022-04-28T19:45:31Z 2022-04-28T19:45:31Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1145/3470482.3479618 ACM International Conference Proceeding Series, p. 61-64. http://hdl.handle.net/11449/222587 10.1145/3470482.3479618 2-s2.0-85116586541 |
url |
http://dx.doi.org/10.1145/3470482.3479618 http://hdl.handle.net/11449/222587 |
identifier_str_mv |
ACM International Conference Proceeding Series, p. 61-64. 10.1145/3470482.3479618 2-s2.0-85116586541 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ACM International Conference Proceeding Series |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
61-64 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1808128960953843712 |