A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept
| Autor: | José Miguel Ramírez-Sanz, José Luis Garrido-Labrador, Alicia Olivares-Gil, Álvaro García-Bustillo, Álvar Arnaiz-González, José-Francisco Díez-Pastor, Maha Jahouh, Josefa González-Santos, Jerónimo J. González-Bernal, Marta Allende-Río, Florita Valiñas-Sieiro, Jose M. Trejo-Gabriel-Galan, Esther Cubo |
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| Rok vydání: | 2023 |
| Předmět: |
Informática
Medicina Leadership and Management Health Policy Salud Health Informatics Computer science Telemedicine Parkinson’s disease telerehabilitation telemedicine big data artificial intelligence in healthcare Big data Neurology Health Information Management Health Medicine Artificial intelligence in healthcare Neurología Telerehabilitation |
| Zdroj: | Healthcare; Volume 11; Issue 4; Pages: 507 |
| ISSN: | 2227-9032 |
| DOI: | 10.3390/healthcare11040507 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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