A gesture recognition library for the therapy domain and its applications
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFPE |
Texto Completo: | https://repositorio.ufpe.br/handle/123456789/27080 |
Resumo: | The computational implementation of human body gestures recognition has been a challenge for several years. Nowadays, thanks to the development of RGB-D cameras it is possible to acquire a set of data that represents a human position in space. Despite that, these cameras provide raw data, still being a problem to identify in real-time a specific pre-defined user movement continuously which can then be applied in applications as, for example, the tracking of physiotherapeutic movements or exercises. This work presents two new techniques to identify gestures, both having physiotherapeutic concerns about the performed exercise; one is based on physiotherapeutic standards, the biomechanical planes, while the other aims to recognize the functional exercises and is based on a concept called checkpoints. Both these techniques were tested and validated by physiotherapists from the Physiotherapy Department at the Federal University of Pernambuco. The techniques were also integrated in a library which was then used in two case studies and two general applications where their applicability was tested in physiotherapeutic and non-physiotherapeutic domains obtaining good results and showing that they can be used on general applications as well. |
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CHAVES, Thiago de Menezeshttp://lattes.cnpq.br/2821966126501304http://lattes.cnpq.br/3355338790654065TEICHRIEB, Verônica2018-09-27T21:36:20Z2018-09-27T21:36:20Z2016-03-11https://repositorio.ufpe.br/handle/123456789/27080The computational implementation of human body gestures recognition has been a challenge for several years. Nowadays, thanks to the development of RGB-D cameras it is possible to acquire a set of data that represents a human position in space. Despite that, these cameras provide raw data, still being a problem to identify in real-time a specific pre-defined user movement continuously which can then be applied in applications as, for example, the tracking of physiotherapeutic movements or exercises. This work presents two new techniques to identify gestures, both having physiotherapeutic concerns about the performed exercise; one is based on physiotherapeutic standards, the biomechanical planes, while the other aims to recognize the functional exercises and is based on a concept called checkpoints. Both these techniques were tested and validated by physiotherapists from the Physiotherapy Department at the Federal University of Pernambuco. The techniques were also integrated in a library which was then used in two case studies and two general applications where their applicability was tested in physiotherapeutic and non-physiotherapeutic domains obtaining good results and showing that they can be used on general applications as well.Implementar um algoritmo computacional de reconhecimento de gesto tem sido um desafio por muitos anos. Hoje em dia, com o desenvolvimento das câmeras RGB-D, é possível adquirir um conjunto de dados que representa a posição de uma pessoa no espaço. Apesar disso, os dados adquiridos por estas câmeras ainda não são suficientes para identificar, em tempo real e de forma contínua, movimentos predefinidos dos usuários, os quais podem ser usados em aplicações como, por exemplo, a análise de movimentos ou exercícios fisioterapêuticos. Este trabalho apresenta duas novas técnicas de reconhecimento de gestos, ambas voltadas ao domínio de fisioterapia; a primeira é baseada em padrões da fisioterapia, chamados de planos biomecânicos, e a segunda tem como propósito reconhecer os gestos realizados durante os exercícios funcionais e é baseada num conceito chamado de checkpoints. Ambas técnicas foram testadas e validadas por fisioterapeutas do Departamento de Fisioterapia da Universidade Federal de Pernambuco. Essas técnicas foram integradas em uma biblioteca, a qual então foi utilizada para desenvolver dois estudos de caso e duas aplicações de propósito gerais, onde suas aplicabilidades foram testadas tanto no domínio fisioterapêutico como fora dele, obtendo bons resultados e mostrando que tais técnicas também podem ser usadas em aplicações gerais.engUniversidade Federal de PernambucoPrograma de Pos Graduacao em Ciencia da ComputacaoUFPEBrasilAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessVisão computacionalReconhecimento de gestosFisioterapiaA gesture recognition library for the therapy domain and its applicationsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesismestradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPETHUMBNAILDISSERTAÇÃO Thiago de Menezes Chaves.pdf.jpgDISSERTAÇÃO Thiago de Menezes Chaves.pdf.jpgGenerated Thumbnailimage/jpeg1290https://repositorio.ufpe.br/bitstream/123456789/27080/5/DISSERTA%c3%87%c3%83O%20Thiago%20de%20Menezes%20Chaves.pdf.jpgffade813e28e77eb6b6de86aef842fc5MD55ORIGINALDISSERTAÇÃO Thiago de Menezes Chaves.pdfDISSERTAÇÃO Thiago de Menezes Chaves.pdfapplication/pdf8950056https://repositorio.ufpe.br/bitstream/123456789/27080/1/DISSERTA%c3%87%c3%83O%20Thiago%20de%20Menezes%20Chaves.pdfd71486339c8fdddafbd8f55d482f1d70MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.pt_BR.fl_str_mv |
A gesture recognition library for the therapy domain and its applications |
title |
A gesture recognition library for the therapy domain and its applications |
spellingShingle |
A gesture recognition library for the therapy domain and its applications CHAVES, Thiago de Menezes Visão computacional Reconhecimento de gestos Fisioterapia |
title_short |
A gesture recognition library for the therapy domain and its applications |
title_full |
A gesture recognition library for the therapy domain and its applications |
title_fullStr |
A gesture recognition library for the therapy domain and its applications |
title_full_unstemmed |
A gesture recognition library for the therapy domain and its applications |
title_sort |
A gesture recognition library for the therapy domain and its applications |
author |
CHAVES, Thiago de Menezes |
author_facet |
CHAVES, Thiago de Menezes |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/2821966126501304 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/3355338790654065 |
dc.contributor.author.fl_str_mv |
CHAVES, Thiago de Menezes |
dc.contributor.advisor1.fl_str_mv |
TEICHRIEB, Verônica |
contributor_str_mv |
TEICHRIEB, Verônica |
dc.subject.por.fl_str_mv |
Visão computacional Reconhecimento de gestos Fisioterapia |
topic |
Visão computacional Reconhecimento de gestos Fisioterapia |
description |
The computational implementation of human body gestures recognition has been a challenge for several years. Nowadays, thanks to the development of RGB-D cameras it is possible to acquire a set of data that represents a human position in space. Despite that, these cameras provide raw data, still being a problem to identify in real-time a specific pre-defined user movement continuously which can then be applied in applications as, for example, the tracking of physiotherapeutic movements or exercises. This work presents two new techniques to identify gestures, both having physiotherapeutic concerns about the performed exercise; one is based on physiotherapeutic standards, the biomechanical planes, while the other aims to recognize the functional exercises and is based on a concept called checkpoints. Both these techniques were tested and validated by physiotherapists from the Physiotherapy Department at the Federal University of Pernambuco. The techniques were also integrated in a library which was then used in two case studies and two general applications where their applicability was tested in physiotherapeutic and non-physiotherapeutic domains obtaining good results and showing that they can be used on general applications as well. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016-03-11 |
dc.date.accessioned.fl_str_mv |
2018-09-27T21:36:20Z |
dc.date.available.fl_str_mv |
2018-09-27T21:36:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufpe.br/handle/123456789/27080 |
url |
https://repositorio.ufpe.br/handle/123456789/27080 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pos Graduacao em Ciencia da Computacao |
dc.publisher.initials.fl_str_mv |
UFPE |
dc.publisher.country.fl_str_mv |
Brasil |
publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFPE instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
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UFPE |
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UFPE |
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Repositório Institucional da UFPE |
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Repositório Institucional da UFPE |
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