A Machine Learning App for Monitoring Physical Therapy at Home

Detalhes bibliográficos
Autor(a) principal: Pereira, Bruno
Data de Publicação: 2023
Outros Autores: Cunha, Bruno, Viana, Paula, Lopes, Maria, Melo, Ana S. C., Sousa, Andreia S. P.
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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spelling 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
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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
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