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dc.contributor.authorRamírez Sanz, José Miguel 
dc.contributor.authorGarrido Labrador, José Luis 
dc.contributor.authorOlivares Gil, Alicia 
dc.contributor.authorGarcía Bustillo, Álvaro 
dc.contributor.authorArnaiz González, Álvar 
dc.contributor.authorDiez Pastor, José Francisco 
dc.contributor.authorJahouh, Maha
dc.contributor.authorGonzález Santos, Josefa 
dc.contributor.authorGonzález Bernal, Jerónimo 
dc.contributor.authorAllende-Río, Marta
dc.contributor.authorValiñas Sieiro, Florita 
dc.contributor.authorTrejo Gabriel y Galán, José Mª
dc.contributor.authorCubo Delgado, Esther 
dc.date.accessioned2024-02-09T12:15:55Z
dc.date.available2024-02-09T12:15:55Z
dc.date.issued2023-02
dc.identifier.issn2227-9032
dc.identifier.urihttp://hdl.handle.net/10259/8667
dc.description.abstractThe consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.en
dc.description.sponsorshipThis work was supported by project PI19/00670 of the Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, Spain. The authors gratefully acknowledge the support of the NVIDIA Corporation and its donation of the TITAN Xp GPU used in this research. In addition, this work was partially supported by the European Social Fund, as the authors José Miguel Ramírez-Sanz, José Luis Garrido-Labrador, and Alicia Olivares-Gil are the recipients of a pre-doctoral grant (EDU/875/2021) from the Conserjería de Educación de la Junta de Castilla y León.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofHealthcare. 2023, V. 11, n. 4, 507es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectParkinson’s diseaseen
dc.subjectTelerehabilitationen
dc.subjectTelemedicineen
dc.subjectBig dataen
dc.subjectArtificial intelligence in healthcareen
dc.subject.otherSistema nervioso-Enfermedadeses
dc.subject.otherNervous system-Diseasesen
dc.subject.otherMedicinaes
dc.subject.otherMedicineen
dc.subject.otherTerapéuticaes
dc.subject.otherTherapeuticsen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concepten
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/healthcare11040507es
dc.identifier.doi10.3390/healthcare11040507
dc.identifier.essn2227-9032
dc.journal.titleHealthcarees
dc.volume.number11es
dc.issue.number4es
dc.page.initial507es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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