Upper Limb Motion Tracking and Classification: A Smartphone Approach

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Sergipe (UFS)-
Autor(es): dc.contributorBrazilian Institute of Neuroscience and Neurotechnology-BRAINN-
Autor(es): dc.creatorRodrigues, Luis. G. S.-
Autor(es): dc.creatorDias, Diego R. C.-
Autor(es): dc.creatorGuimarães, Marcelo P.-
Autor(es): dc.creatorBrandão, Alexandre F.-
Autor(es): dc.creatorRocha, Leonardo C. D.-
Autor(es): dc.creatorIope, Rogério L.-
Autor(es): dc.creatorBrega, José R. F.-
Data de aceite: dc.date.accessioned2025-08-21T19:05:51Z-
Data de disponibilização: dc.date.available2025-08-21T19:05:51Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-11-04-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1145/3470482.3479618-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/222587-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/222587-
Descrição: dc.descriptionDue 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.-
Descrição: dc.descriptionSão Paulo State University-UNESP-
Descrição: dc.descriptionFederal University of São João Del-Rei-UFSJ Brazil and Brazilian Institute of Neuroscience and Neurotechnology-BRAINN-
Descrição: dc.descriptionBrazilian Institute of Neuroscience and Neurotechnology-BRAINN-
Descrição: dc.descriptionFederal University of São João Del-Rei-UFSJ-
Descrição: dc.descriptionSão Paulo State University-UNESP Brazil and Brazilian Institute of Neuroscience and Neurotechnology-BRAINN-
Descrição: dc.descriptionSão Paulo State University-UNESP-
Descrição: dc.descriptionSão Paulo State University-UNESP Brazil and Brazilian Institute of Neuroscience and Neurotechnology-BRAINN-
Formato: dc.format61-64-
Idioma: dc.languageen-
Relação: dc.relationACM International Conference Proceeding Series-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectaugmented reality-
Palavras-chave: dc.subjectComputer vision-
Palavras-chave: dc.subjectmotion capture-
Palavras-chave: dc.subjectsupervised machine learning-
Título: dc.titleUpper Limb Motion Tracking and Classification: A Smartphone Approach-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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