A Machine Learning App for Monitoring Physical Therapy at Home
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/10400.22/24729 |
Resumo: | Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research. |
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A Machine Learning App for Monitoring Physical Therapy at Homepose estimation; exercise evaluation; mobile health; remote monitoring; rehabilitationShoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.MDPIRepositório Científico do Instituto Politécnico do PortoPereira, BrunoCunha, BrunoViana, PaulaLopes, MariaMelo, Ana S. C.Sousa, Andreia S. P.2024-01-29T08:23:49Z2023-12-272023-12-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/24729eng1. Pereira B, Cunha B, Viana P, Lopes M, Melo ASC, Sousa ASP (2023). A Machine Learning App for Monitoring Physical Therapy at Home. Sensors. 2024; 24(1):158. https://doi.org/10.3390/s2401015810.3390/s24010158info: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:RCAAP2024-01-31T01:50:41Zoai:recipp.ipp.pt:10400.22/24729Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:59:06.553623Repositório 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 |
A Machine Learning App for Monitoring Physical Therapy at Home |
title |
A Machine Learning App for Monitoring Physical Therapy at Home |
spellingShingle |
A Machine Learning App for Monitoring Physical Therapy at Home Pereira, Bruno pose estimation; exercise evaluation; mobile health; remote monitoring; rehabilitation |
title_short |
A Machine Learning App for Monitoring Physical Therapy at Home |
title_full |
A Machine Learning App for Monitoring Physical Therapy at Home |
title_fullStr |
A Machine Learning App for Monitoring Physical Therapy at Home |
title_full_unstemmed |
A Machine Learning App for Monitoring Physical Therapy at Home |
title_sort |
A Machine Learning App for Monitoring Physical Therapy at Home |
author |
Pereira, Bruno |
author_facet |
Pereira, Bruno Cunha, Bruno Viana, Paula Lopes, Maria Melo, Ana S. C. Sousa, Andreia S. P. |
author_role |
author |
author2 |
Cunha, Bruno Viana, Paula Lopes, Maria Melo, Ana S. C. Sousa, Andreia S. P. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Pereira, Bruno Cunha, Bruno Viana, Paula Lopes, Maria Melo, Ana S. C. Sousa, Andreia S. P. |
dc.subject.por.fl_str_mv |
pose estimation; exercise evaluation; mobile health; remote monitoring; rehabilitation |
topic |
pose estimation; exercise evaluation; mobile health; remote monitoring; rehabilitation |
description |
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-27 2023-12-27T00:00:00Z 2024-01-29T08:23:49Z |
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/10400.22/24729 |
url |
http://hdl.handle.net/10400.22/24729 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1. Pereira B, Cunha B, Viana P, Lopes M, Melo ASC, Sousa ASP (2023). A Machine Learning App for Monitoring Physical Therapy at Home. Sensors. 2024; 24(1):158. https://doi.org/10.3390/s24010158 10.3390/s24010158 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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 |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1817552278444310528 |