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@article{ramirez-sanz2023,

title = {A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept},

author = {José Miguel Ramírez-Sanz and José Luis Garrido-Labrador and Alicia Olivares-Gil and Álvaro García-Bustillo and Álvar Arnaiz-González and José-Francisco Díez-Pastor and Maha Jahouh and Josefa González-Santos and Jerónimo J. González-Bernal and Marta Allende-Río and Florita Valiñas-Sieiro and Jose M. Trejo-Gabriel-Galan and Esther Cubo},

editor = {Maria-Esther Vidal and José Alberto Benítez Andrades and Alejandro Rodríguez-González},

url = {https://www.mdpi.com/2227-9032/11/4/507},

doi = {10.3390/healthcare11040507},

issn = {2227-9032},

year  = {2023},

date = {2023-02-09},

urldate = {2023-02-09},

journal = {Healthcare},

volume = {11},

number = {507},

issue = {4},

abstract = {first_page 

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Open AccessArticle 

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept 

by José Miguel Ramírez-Sanz 

1 [ORCID] , José Luis Garrido-Labrador 

1 [ORCID] , Alicia Olivares-Gil 

1 [ORCID] , Álvaro García-Bustillo 

2 [ORCID] , Álvar Arnaiz-González 

1,* [ORCID] , José-Francisco Díez-Pastor 

1, Maha Jahouh 

3, Josefa González-Santos 

3, Jerónimo J. González-Bernal 

3 [ORCID] , Marta Allende-Río 

4, Florita Valiñas-Sieiro 

4, Jose M. Trejo-Gabriel-Galan 

4 and Esther Cubo 

4 

1 

Escuela Politécnica Superior, Departamento de Ingeniería Informática, Universidad de Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain 

2 

Fundación Burgos por la Investigación de la Salud, 09006 Burgos, Spain 

3 

Departamento de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Paseo Comendadores s/n, 09001 Burgos, Spain 

4 

Servicio de Neurología, Hospital Universitario de Burgos, 09006 Burgos, Spain 

* 

Author to whom correspondence should be addressed. 

Healthcare 2023, 11(4), 507; https://doi.org/10.3390/healthcare11040507 (registering DOI) 

Received: 19 December 2022 / Revised: 20 January 2023 / Accepted: 7 February 2023 / Published: 9 February 2023 

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics) 

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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.},

keywords = {artificial intelligence in healthcare, Big data, Parkinson's disease, telemedicine, telerehabilitation},

pubstate = {published},

tppubtype = {article}

}