2026-04-17T18:52:27Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/75362023-04-18T10:42:52Zcom_10259_4141com_10259_5086com_10259_2604com_10259_4219com_10259_4996com_10259_3989com_10259.4_106com_10259_5377com_10259_6688col_10259_4142col_10259_4220col_10259_6689col_10259_4999col_10259_5378

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept Ramírez Sanz, José Miguel Garrido Labrador, José Luis Olivares Gil, Alicia García Bustillo, Álvaro Arnaiz González, Álvar Diez Pastor, José Francisco Jahouh, Maha González Santos, Josefa González Bernal, Jerónimo Allende-Río, Marta Valiñas Sieiro, Florita Trejo Gabriel y Galán, José Mª Cubo Delgado, Esther Parkinson’s disease Telerehabilitation Telemedicine Big data Artificial intelligence in healthcare 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. 2023-03-13T12:15:33Z 2023-03-13T12:15:33Z 2023-03-13T12:15:33Z 2023-02 info:eu-repo/semantics/article http://hdl.handle.net/10259/7536 10.3390/healthcare11040507 2227-9032 eng Healthcare. 2023, V. 11, n. 4, 507 https://doi.org/10.3390/healthcare11040507 info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII)/PI19%2F00670/ES/ESTUDIO DE FACTIBILIDAD Y COSTE-EFECTIVIDAD DEL USO TELEMEDICINA CON UN EQUIPO MULTIDISCIPLINAR PARA PREVENCION DE CAIDAS EN LA ENFERMEDAD DE PARKINSON/ http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI