Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/11110/2564 https://doi.org/10.3390/machines11020234 |
Resumo: | Regardless of the type of sport, coaches must perform the difficult task of evaluating the performance of athletes. In some cases, this task is aided using technology which provides tools for this purpose. When the sport considered is taekwondo this scenario does not apply as the athlete evaluation methods used are mostly manual. Thus, the project presented in this paper has the main objective to develop a system that can be used as a tool to evaluate the performance of taekwondo athletes in real time, with special attention to the low cost of implementation and ease of use. With the intention of meeting these requirements, the developed system comprises a 3D camera with a depth sensor (Orbbec Astra), Inertial Measurement Units (IMUs) with accelerometer and gyroscope, a computer and specific software developed for this purpose. This system allows the collection of data from the athletes’ movements necessary for the creation of a dataset that is then analyzed and interpretated. The system permits the user to obtain real-time information about the speed, acceleration, and strength of the athlete’s limbs during training as well as the identification of some movements and their accounting. To achieve this functionality, deep-learning architecture models were used, more specifically long short-term memory (LSTM). The intention is to provide a new training methodology through faster feedback, so that a faster evolution of the athlete’s performance is possible, contributing to the technological development of the assessment practices used in taekwondo. |
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Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project DescriptionDeep-learningmotion analysisneural networkswearablescomputer visiontaekwondoRegardless of the type of sport, coaches must perform the difficult task of evaluating the performance of athletes. In some cases, this task is aided using technology which provides tools for this purpose. When the sport considered is taekwondo this scenario does not apply as the athlete evaluation methods used are mostly manual. Thus, the project presented in this paper has the main objective to develop a system that can be used as a tool to evaluate the performance of taekwondo athletes in real time, with special attention to the low cost of implementation and ease of use. With the intention of meeting these requirements, the developed system comprises a 3D camera with a depth sensor (Orbbec Astra), Inertial Measurement Units (IMUs) with accelerometer and gyroscope, a computer and specific software developed for this purpose. This system allows the collection of data from the athletes’ movements necessary for the creation of a dataset that is then analyzed and interpretated. The system permits the user to obtain real-time information about the speed, acceleration, and strength of the athlete’s limbs during training as well as the identification of some movements and their accounting. To achieve this functionality, deep-learning architecture models were used, more specifically long short-term memory (LSTM). The intention is to provide a new training methodology through faster feedback, so that a faster evolution of the athlete’s performance is possible, contributing to the technological development of the assessment practices used in taekwondo.Machines2023-03-06T09:59:13Z2023-03-062023-02-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/11110/2564https://doi.org/10.3390/machines11020234http://hdl.handle.net/11110/2564engCunha, PedroBarbosa, PauloFerreira, FábioSilva, TâniaMartins, NunoSoares, FilomenaCarvalho, Vítorinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-09T04:40:29Zoai:ciencipca.ipca.pt:11110/2564Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:10:55.618686Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description |
title |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description |
spellingShingle |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description Cunha, Pedro Deep-learning motion analysis neural networks wearables computer vision taekwondo |
title_short |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description |
title_full |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description |
title_fullStr |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description |
title_full_unstemmed |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description |
title_sort |
Cyber-Physical System for Evaluation of Taekwondo Athletes: An Initial Project Description |
author |
Cunha, Pedro |
author_facet |
Cunha, Pedro Barbosa, Paulo Ferreira, Fábio Silva, Tânia Martins, Nuno Soares, Filomena Carvalho, Vítor |
author_role |
author |
author2 |
Barbosa, Paulo Ferreira, Fábio Silva, Tânia Martins, Nuno Soares, Filomena Carvalho, Vítor |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Cunha, Pedro Barbosa, Paulo Ferreira, Fábio Silva, Tânia Martins, Nuno Soares, Filomena Carvalho, Vítor |
dc.subject.por.fl_str_mv |
Deep-learning motion analysis neural networks wearables computer vision taekwondo |
topic |
Deep-learning motion analysis neural networks wearables computer vision taekwondo |
description |
Regardless of the type of sport, coaches must perform the difficult task of evaluating the performance of athletes. In some cases, this task is aided using technology which provides tools for this purpose. When the sport considered is taekwondo this scenario does not apply as the athlete evaluation methods used are mostly manual. Thus, the project presented in this paper has the main objective to develop a system that can be used as a tool to evaluate the performance of taekwondo athletes in real time, with special attention to the low cost of implementation and ease of use. With the intention of meeting these requirements, the developed system comprises a 3D camera with a depth sensor (Orbbec Astra), Inertial Measurement Units (IMUs) with accelerometer and gyroscope, a computer and specific software developed for this purpose. This system allows the collection of data from the athletes’ movements necessary for the creation of a dataset that is then analyzed and interpretated. The system permits the user to obtain real-time information about the speed, acceleration, and strength of the athlete’s limbs during training as well as the identification of some movements and their accounting. To achieve this functionality, deep-learning architecture models were used, more specifically long short-term memory (LSTM). The intention is to provide a new training methodology through faster feedback, so that a faster evolution of the athlete’s performance is possible, contributing to the technological development of the assessment practices used in taekwondo. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03-06T09:59:13Z 2023-03-06 2023-02-02T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/11110/2564 https://doi.org/10.3390/machines11020234 http://hdl.handle.net/11110/2564 |
url |
http://hdl.handle.net/11110/2564 https://doi.org/10.3390/machines11020234 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Machines |
publisher.none.fl_str_mv |
Machines |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131206732742656 |