RT info:eu-repo/semantics/article
T1 A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept
A1 Ramírez Sanz, José Miguel
A1 Garrido Labrador, José Luis
A1 Olivares Gil, Alicia
A1 García Bustillo, Álvaro
A1 Arnaiz González, Álvar
A1 Diez Pastor, José Francisco
A1 Jahouh, Maha
A1 González Santos, Josefa
A1 González Bernal, Jerónimo
A1 Allende-Río, Marta
A1 Valiñas Sieiro, Florita
A1 Trejo Gabriel y Galán, José Mª
A1 Cubo Delgado, Esther
K1 Parkinson’s disease
K1 Telerehabilitation
K1 Telemedicine
K1 Big data
K1 Artificial intelligence in healthcare
K1 Sistema nervioso-Enfermedades
K1 Nervous system-Diseases
K1 Medicina
K1 Medicine
K1 Terapéutica
K1 Therapeutics
K1 Informática
K1 Computer science
AB 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.
PB MDPI
SN 2227-9032
YR 2023
FD 2023-02
LK http://hdl.handle.net/10259/8667
UL http://hdl.handle.net/10259/8667
LA eng
NO This 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 09-may-2024