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Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation

2024Conference paper Restricted access
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dc.contributor.authorKlein, Luan C.
dc.contributor.authorChellal, Arezki Abderrahim
dc.contributor.authorGrilo, Vinicius F.S.B.
dc.contributor.authorGonçalves, José
dc.contributor.authorPacheco, Maria F.
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorMonteiro, Fernando C.
dc.contributor.authorLima, José
dc.date.accessioned2024-10-08T14:46:00Z
dc.date.available2024-10-08T14:46:00Z
dc.date.issued2024
dc.description.abstractAngle assessment is crucial in rehabilitation and significantly influences physiotherapists’ decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around 5◦ and 17◦, respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easyto- implement solution.pt_PT
dc.description.sponsorshipThis work has been supported by SmartHealth - Inteligência Artificial para Cuidados de Saúde Personalizados ao Longo da Vida, under the project ref. NORTE-01-0145-FEDER-000045. The authors are grateful to the Foundation for Science and Technology (FCT) for financial support under ref. FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Arezki A. Chellal is grateful to the FCT Foundation for its support through the FCT PhD scholarship with ref. UI/BD/154484/2022.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationKlein, Luan C.; Chellal, Arezki Abderrahim; Grilo, Vinicius; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, José (2024). Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 3–18. ISBN 978-3-031-53035-7pt_PT
dc.identifier.doi10.1007/978-3-031-53036-4_1pt_PT
dc.identifier.isbn978-3-031-53035-7
dc.identifier.isbn978-3-031-53036-4
dc.identifier.urihttp://hdl.handle.net/10198/30381
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectJoin Angle Measurementpt_PT
dc.subjectArtificial Intelligencept_PT
dc.subjectMotion Capturept_PT
dc.subjectRobotic Rehabilitationpt_PT
dc.titleAssessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitationpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_NORTE/UI%2FBD%2F154484%2F2022/PT
oaire.citation.endPage18pt_PT
oaire.citation.startPage3pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
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person.givenNameArezki Abderrahim
person.givenNameJosé
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person.givenNameFlorbela P.
person.givenNameFernando C.
person.givenNameJosé
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project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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