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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7536

    Título
    A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept
    Autor
    Ramírez Sanz, José MiguelUBU authority Orcid
    Garrido Labrador, José LuisUBU authority Orcid
    Olivares Gil, AliciaUBU authority Orcid
    García Bustillo, ÁlvaroUBU authority Orcid
    Arnaiz González, ÁlvarUBU authority Orcid
    Diez Pastor, José FranciscoUBU authority Orcid
    Jahouh, Maha
    González Santos, JosefaUBU authority Orcid
    González Bernal, JerónimoUBU authority Orcid
    Allende-Río, Marta
    Valiñas Sieiro, FloritaUBU authority
    Trejo Gabriel y Galán, José Mª
    Cubo Delgado, EstherUBU authority Orcid
    Publicado en
    Healthcare. 2023, V. 11, n. 4, 507
    Editorial
    MDPI
    Fecha de publicación
    2023-02
    DOI
    10.3390/healthcare11040507
    Abstract
    The 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.
    Palabras clave
    Parkinson’s disease
    Telerehabilitation
    Telemedicine
    Big data
    Artificial intelligence in healthcare
    Materia
    Informática
    Computer science
    Salud
    Health
    Medicina
    Medicine
    Neurología
    Neurology
    URI
    http://hdl.handle.net/10259/7536
    Versión del editor
    https://doi.org/10.3390/healthcare11040507
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    Atribución 4.0 Internacional
    Documento(s) sujeto(s) a una licencia Creative Commons Atribución 4.0 Internacional
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    Ramirez-healthcare_2023.pdf
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