2026-06-17T20:30:23Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/88602024-03-21T01:05:17Zcom_10259_4415com_10259_3989com_10259.4_106com_10259_2604com_10259_4219com_10259_5086col_10259_4416col_10259_4220

Amo Salas, Javier Olivares Gil, Alicia García Bustillo, Álvaro García García, David Arnaiz González, Álvar Cubo Delgado, Esther Parkinson’s disease Finger tapping Machine learning Computer vision Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up. 2024-03-20 2024-03-20 2024-02 info:eu-repo/semantics/article http://hdl.handle.net/10259/8860 10.3390/healthcare12040439 2227-9032 eng Healthcare. 2024, V. 12, n. 4, 439 https://doi.org/10.3390/healthcare12040439 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI